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  • 101.
    Gálvez, Antonio
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Basque Research and Technology Alliance (BRTA),Derio -Vizcaya, 48170, Spain.
    Diez-Olivan, Alberto
    TECNALIA, Basque Research and Technology Alliance (BRTA),Derio -Vizcaya, 48170, Spain.
    Seneviratne, Dammika
    TECNALIA, Basque Research and Technology Alliance (BRTA),Derio -Vizcaya, 48170, Spain.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Synthetic Data Generation in Hybrid Modelling of Railway HVAC System2020In: 17th IMEKO TC 10 and EUROLAB Virtual Conference: “Global Trends in Testing, Diagnostics & Inspection for 2030” / [ed] Zsolt János Viharos; Lorenzo Ciani; Piotr Bilski; Mladen Jakovcic, International Measurement Confederation (IMEKO) , 2020, p. 79-84Conference paper (Refereed)
    Abstract [en]

    This paper proposes a hybrid model (HyM)for a heating, ventilation and air conditioning (HVAC) system installed in a passenger train. This HyM fuses data from two sources: data taken from the real system and synthetic data generated using a physics-based model of the HVAC.

    The physical model of the HVAC was developed to include the sensors located in the real system and new virtual sensors reproducing the behaviour of the system while a failure mode (FM) is simulated.

    Statistical features are calculated from the selected signals. These features are labelled according to the related FMs and are merged with the features calculated from the data from the real system. This data fusion allows us to classify the condition indicators of the system according to the FMs. The merged features are used to train a neural network (NN), which achieves a remarkable accuracy.

    Accuracy is a key concern of future research on the detection and diagnosis of a multiple faults and the estimation of the remaining useful life (RUL) through prognosis. The outcome is beneficial for the proper functioning of the system and the safety of the passengers.

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  • 102.
    Gálvez, Antonio
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain.
    Rubio, Jokin
    TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain.
    Seneviratne, Dammika
    TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain.
    Gonzalez, Asier
    TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain.
    Jimenez, Alberto
    TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain.
    Martinez-de-Estarrona, Unai
    TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain.
    Juuso, Esko
    Control Engineering Group, Faculty of Technology, University of Oulu, 90014 Oulu, Finland.
    Hybrid Models and Digital Twins for Condition Monitoring: HVAC System for Railway2021In: Simulation Notes Europe, ISSN 2306-0271, Vol. 31, no 3, p. 121-126Article in journal (Refereed)
    Abstract [en]

    Safety passenger transportation is more important than efficiency or reliability. Therefore, it is vital to maintain the proper condition of the equipment related to the passengers’ comfort and safety. This manuscript presents the methodology of complete development and implementation of both hybrid model and digital twin 3.0 for an HVAC in railways. The objective of this is to monitor the condition of the HVAC where it matters to the comfort and safety of the passengers in the trains. The level 3.0 of digital twin will be developed for the diagnosis and prognosis of HVAC by using hybrid modeling. The description illustrated in this paper is focused on the methodology used to implement a hybrid model-based approach, and both the need and advantages of using hybrid model approaches instead of data-based approaches. The development considers the importance of safety and environmental risks, which are included in the risk quantification of failure modes. Railway’s maintainers replace critical components in early stages of degradation; thus, the use of a data-driven model loses essential information related to advanced stages of degradation which might decrease the accuracy of the maintenance instructions provided. Physics-based model can be used to generate synthetic data to overcome the lack of data in advanced stages of degradation, and then, the synthetic data can be combined with the real data, which is collected by sensor located in the real system, to build the data-driven model. The combination leads to form hybrid-model based approach with a large number of failure modes that were unpredictable. Finally, the outcome is beneficial for the proper functioning of systems; hence, safety of the passengers. 

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  • 103.
    Gálvez, Antonio
    et al.
    Tecnalia Research and Innovation, 48160 Derio, Spain.
    Rubio, Jokin
    Tecnalia Research and Innovation, 48160 Derio, Spain.
    Severiratne, Dammika
    Tecnalia Research and Innovation, 48160 Derio, Spain.
    González, Asier
    Tecnalia Research and Innovation, 48160 Derio, Spain.
    Jimenez, Alberto
    Tecnalia Research and Innovation, 48160 Derio, Spain.
    Martinez-de-Estarrona, Unai
    Tecnalia Research and Innovation, 48160 Derio, Spain.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Tecnalia Research and Innovation, 48160 Derio, Spain.
    Juuso, Esko
    Control Engineering Group, Faculty of Technology, University of Oulu, 90014 Oulu, Finland.
    Hybrid Models and Digital Twins for Condition Monitoring: HVAC System for Railway2019In: EUROSIM 2019: Extended Abstract Volume / [ed] Emilio Jiménez, Juan Ignacio Latorre, ARGESIM Publisher , 2019, p. 52-52Conference paper (Other academic)
    Abstract [en]

    The passenger transportation,safety is more important than efficiency or reliability. Therefore, it is vital to maintain the proper condition of the equipment related to the passengers’ comfort and safety.This manuscriptexplainsthe methodology ofcomplete development and implementation of both hybrid model and digital twin3.0for a HVAC inrailways. The objective of this is to monitor the condition of the HVAC where it matters to the comfort and safety of the passengers in the trains. The level3.0of digital twinwill be develop for the diagnosis and prognosis of HVAC by using the hybrid modeling.The descriptionillustratedin this paper is focused on the methodology used to implement the hybrid model approach and both the need and advantages to use a hybrid model approach instead of thedata-based approach.One of the particularitiesconsidered doing the developmentwas the importanceof the safety and environmental risk whichwere included in the risk quantification of the failure modes. In train companies the maintainers replace critical components in early stages of degradation, thus, using a data-based model might loses important information and does not givegood support to manage the maintenance instructions.Developing a physicsbased-model will be able to generate synthetic data for the behavior of the components in advanced stages of degradation and combiningitwith the data-based modellead to formhybrid model with a large number of failure modes that were unpredictable. Finally,the outcome is beneficialfor the proper functioning of the systems, hence, the safety of the passengers.

  • 104.
    Gálvez, Antonio
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio-Vizcaya, Spain.
    Seneviratne, Dammika
    TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio-Vizcaya, Spain.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio-Vizcaya, Spain.
    Hybrid Model Development for HVAC System in Transportation2021In: Technologies, E-ISSN 2227-7080, Vol. 9, no 1, article id 18Article in journal (Refereed)
    Abstract [en]

    Hybrid models combine physics-based models and data-driven models. This combination is a useful technique to detect fault and predict the current degradation of equipment. This paper proposes a physics-based model, which will be part of a hybrid model, for a heating, ventilation, and air conditioning system installed in the passenger vehicle of a train. The physics-based model is divided into four main parts: heating subsystems, cooling subsystems, ventilation subsystems, and cabin thermal networking subsystems. These subsystems are developed when considering the sensors that are located in the real system, so the model can be linked via the acquired sensor data and virtual sensor data to improve the detectability of failure modes. Thus, the physics-based model can be synchronized with the real system to provide better simulation results. The paper also considers diagnostics and prognostics performance. First, it looks at the current situation of the maintenance strategy for the heating, ventilation, air conditioning system, and the number of failure modes that the maintenance team can detect. Second, it determines the expected improvement using hybrid modelling to maintain the system. This improvement is based on the capabilities of detecting new failure modes. The paper concludes by suggesting the future capabilities of hybrid models.

  • 105.
    Gálvez, Antonio
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Basque Research and Technology Alliance (BRTA), 48170, Derio-Vizcaya, Spain.
    Seneviratne, Dammika
    TECNALIA, Basque Research and Technology Alliance (BRTA), 48170, Derio-Vizcaya, Spain.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Basque Research and Technology Alliance (BRTA), 48170, Derio-Vizcaya, Spain.
    Juuso, Esko
    Control Engineering Group, Faculty of Technology, University of Oulu, PO Box 4300, FI-90014, Oulu, Finland.
    Feature Assessment for a Hybrid Model2023In: Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021 / [ed] Esko Juuso & Diego Galar, Springer Nature, 2023, p. 43-58Conference paper (Refereed)
    Abstract [en]

    This paper proposes an assessment of features orientated to improve the accuracy of a hybrid model (HyM) used for detecting faults in a heating, ventilation, and air conditioning (HVAC) system. The HyM combines data collected by sensors embedded in the system with data generated by a physics-based model of the HVAC. The physics-based model includes sensors embedded in the real system and virtual sensors to represent the behaviour of the system when a failure mode (FM) is simulated. This fusion leads to improved maintenance actions to reduce the number of failures and predict the behaviour of the system. HyM can lead to improved fault detection and diagnostics (FDD) processes of critical systems, but multiple fault detection models are sometimes inaccurate. The paper assesses features extracted from synthetic signals. The results of the assessment are used to improve the accuracy of a multiple fault detection model developed in previous research. The assessment of features comprises the following: (1) generation of run-to-failure data using the physics-based model of the HVAC system; the FMs simulated in this paper are dust in the air filter, degradation of the CO2 sensor, degradation of the evaporator fan, and variations in the compression rate of the cooling system; (2) identification of the individual features that strongly distinguish the FM; (3) analysis of how the features selected vary when components degrade.

  • 106.
    Hernandez, Angel
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Techniques of Prognostics for Condition-Based Maintenance in Different Types of Assets2014Report (Refereed)
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  • 107.
    Hernandez, Angel
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Perales, Numan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Procedure for rul estimation in industrial assets2014In: Proceedings of the 3rd international workshop and congress on eMaintenance: June 17-18 Luleå, Sweden : eMaintenance, Trends in technologies & methodologies, challenges, possibilites and applications / [ed] Uday Kumar; Ramin Karim; Aditya Parida; Philip Tretten, Luleå: Luleå tekniska universitet, 2014, p. 145-149Conference paper (Refereed)
    Abstract [en]

    Today, prognosis is recognized as a key element of maintenance.However, the implementation of an efficient prognosis tool can becomplicated in industrial and academic sectors, when we speakabout academic sector , we refer to the research centers atuniversities who study the progress and new technologies relatedto prognosis, these centers are very important as they help theimprove maintenance management in the industries. Since it isdifficult to create effective models for different industrial assets.In this context, our general objective is to propose a procedure forimplementing prognosis, from selecting the system or componentto be analyzed to obtaining the estimation of the remaining usefullife. We also explain different approaches to forecasting toestimate remaining useful life, the main objective of prognosis.

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  • 108.
    Hoseinie, Hadi
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ghodrati, Behzad
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Juuso, Esko
    University of Oulu, Control Engineering Group, Faculty of Technology, University of Oulu.
    Optimal Preventive Maintenance Planning for Water Spray System of Drum Shearer2015In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 48, no 17, p. 166-170Article in journal (Refereed)
    Abstract [en]

    Water spray system is one of the most important parts of rock cutting machines, especially the drum shearer. Field data shows that the maintenance of this system is time-consuming and causes major downtimes in the coal mines’ production process. Therefore, it is essential to find an optimum preventive maintenance task and intervals, to reduce the downtime and minimize the associated costs of the machine. In this paper, in order to suggest an optimum preventive maintenance plan, a parametric failure and reliability analysis was done on available data from an Iranian longwall coal mine over the two years. A reliability-based cost modelling was implemented to identify the optimum maintenance interval and frequencies of restoration for the water spray system. In the study, a cost rate function was introduced in which an as-good-as-new effectiveness for restoration actions is considered. The results of the analysis showed that the minimum maintenance cost per unit of time for the studied machine, $19.54/hour, will be achieved within a range of intervals i.e. T=136 hours to T=142 hours.

  • 109.
    Johansson, Carl-Anders
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Villarejo, Roberto
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Monnin, Maxime
    PREDICT.
    Green Condition based Maintenance - an integrated system approach for health assessment and energy optimization of manufacturing machines.2013In: 10th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2013, CM 2013 and MFPT 2013, 2013, Vol. 2, p. 1069-1084Conference paper (Refereed)
    Abstract [en]

    The normal strategy to keep production systems in good conditions is to apply preventive maintenance practices, with a supportive workforce "reactive" in the case of clearly detected malfunctions. This impact on quality, cost and in general, productivity. Added to this, the uncertainty of machine reliability at any given time, also impacts on product/production delivery times. It is known also that a worn-out mechanism can have higher energy consumption. The use of intelligent predictive technologies could contribute to improve the situation, but these techniques are not widely used in the production environment. Often sensors and monitors required for the production environment are non-standard and require costly implementations. Monitoring and profiling the electric current consumption in combination with operational data is an easy to implement Green Condition based Maintenance (Green CBM) technique to improve the overall business effectiveness, under a triple perspective: • Optimizing maintenance strategies based on the prediction of potential failures and schedule maintenance operations in convenient periods and avoid unexpected breakdowns • Operation: Managing energy as a production resource and reduce its consumption • Product reliability: Providing the machine tool builder with real data about the behaviour of the product and their critical components This also opens for new business models for maintenance and service providers. The described Green CBM technique can be applied in many types of machines. In machine tools, focusing on spindles and linear guides, as responsible for the most common and cost-intensive downtimes

  • 110.
    Johansson, Carl-Anders
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Simon, Victor
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Aggregation of electric current consumption features for extraction of maintenance KPIs2014In: Proceedings of Maintenance Performance Measurement and Management (MPMM) Conference 2014 / [ed] José Torres Farinha; Diego Galar, Coimbra: Faculdade de Ciências e Tecnologia da Universidade de Coimbra, Departamento de Engenharia Mecânica , 2014, p. 157-162Conference paper (Refereed)
    Abstract [en]

    For all electric powered machines there is apossibility of extracting information and calculating KeyPerformance Indicators (KPIs) from the electric current signal.Depending on the time window, sampling frequency and type ofanalysis, different indicators from the micro to macro level canbe calculated for such aspects as maintenance, production,energy consumption etc.On the micro-level, the indicators are generally used forcondition monitoring and diagnostics and are normally based ona short time window and a high sampling frequency. The macroindicators are normally based on a longer time window with aslower sampling frequency and are used as indicators for overallperformance, cost or consumption.The indicators can be calculated directly from the currentsignal but can also be based on a combination of informationfrom the current signal and operational data like rpm, positionetc.One or several of those indicators can be used for predictionand prognostics of a machine’s future behaviour.This paper uses this technique to calculate indicators formaintenance and energy optimisation in electric poweredmachines and fleet of machines, especially machine tools.

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  • 111.
    Johansson, Carl-Anders
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Simon, Victor
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Context Driven Remaining Useful Life Estimation2014In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 22, p. 181-185Article in journal (Refereed)
    Abstract [en]

    In the context of maintenance activities maintainers rely on machine information, their past breakdowns, adequate repair methods and guidelines as well as new research results in the area. They usually get access to information and knowledge by using information systems (nondestructive testing (NDT) or condition monitoring.), local databases, e-resources or traditional print media. Basically it can be assumed that, the amount of available information affects the quality of maintenance decision making and acting positively. Machine health information retrieval is the application of information retrieval concepts and techniques to the operation and maintenance domain. Retrieving Contextual information, describing the operational conditions for the machine, is a subarea of information retrieval that incorporates context features in the search process towards its improvement. Both areas have been gaining interest from the research community in order to perform more accurate prognostics according to specific scenarios and happening circumstances. Context is a broad term and in this paper the operational conditions and the way the machine has been used is seen as the context and is represented by operational data collected over time. This paper intends to investigate the effects of the interaction of context features on machine tools health information. This interaction between context and health assessment is bidirectional in the sense that health information seeking behavior can also be used to predict context features that can be used, without disturbing the operational environment and creating production disruptions.The extraction of multiple features from multiple sensors, already deployed in this type of machinery, may constitute snapshots of the current health of certain machine components. The mutation status (the way they have changed) of these snapshots, hereafter called Fingerprints, has been proposed as prognostic marker in machine tools problems. Of them, in this work so far only the spindle fingerprint mutation has been validated independently as prognostic for overall survival and survival after relapse, while the prognostic value of rest of components mutation is still under validation. In this scenario, the prognostic value of spindle fingerprint mutations can be investigated in various contexts defined by stratifications of the machine population.

  • 112.
    Juuso, Esko
    et al.
    University of Oulu, Control Engineering, University of Oulu.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Intelligent Real-Time Risk Analysis for Machines and Process Devices2016In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar; Alireza Ahmadi; Ajit Kumar Verma; Prabhakar Varde, Encyclopedia of Global Archaeology/Springer Verlag, 2016, p. 229-240Conference paper (Refereed)
    Abstract [en]

    Automatic fault detection with condition and stress indices enables reliable condition monitoring to be combined with process control. Useful information on different faults can be obtained by selecting suitable features. Generalised norms can be defined by the order of derivation, the order of the moment and sample time. These norms have the same dimensions as the corresponding signals. The nonlinear scaling used in the linguistic equation approach extends the idea of dimensionless indices to nonlinear systems. The Wöhler curve is represented by a linguistic equation (LE) model. The contribution of the stress is calculated in each sample time, which is taken as a fraction of the cycle time. The cumulative sum of the contributions indicates the degrading of condition and the simulated sums can be used to predict failure time. To avoid high stress situations, the statistical process control (SPC) is extended to nonlinear and non-Gaussian data: the new generalised SPC is suitable for a large set of statistical distributions. It operates without interruptions in short run cases and adapts to the changing process requirements. The scaling functions are updated recursively, which is triggered by a fast increase of the deviation indices. The higher levels, which are rough estimates in the beginning, are gradually refined.

  • 113.
    Juuso, Esko
    et al.
    Control Engineering, Environmental and Chemical Engineering, University of Oulu, Oulu, Finland.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Preface2023In: Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021 / [ed] Esko Juuso; Diego Galar, Springer, 2023, p. v-viConference paper (Other academic)
  • 114.
    Juuso, Esko
    et al.
    Control Engineering, Environmental and Chemical Engineering, University of Oulu, Oulu, Finland.
    Galar, DiegoLuleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 20212023Conference proceedings (editor) (Refereed)
    Abstract [en]

    This volume contains selected papers from the Fifth Conference on Maintenance, Condition Monitoring and Diagnostics, MCMD 2021, in Oulu, Finland, collected by editors with years of experiences in condition monitoring, signal processing, advanced reasoning and diagnostics, maintenance, risk assessment, and asset management. This work maximizes reader insights into the current trends in novel technologies and maintenance trends in industrial domains, energy production and energy conservation, mechatronics and robot technologies. These proceedings discuss key issues and challenges in the operation, maintenance and risk management of complex engineering systems and will serve as a valuable resource for condition monitoring and risk management professionals from industry and science exchange knowledge, experiences and strengthen multidisciplinary network those in the field. This book will be of benefit to academia, and industry alike.

  • 115.
    Kans, Mirka
    et al.
    Linnaeus University.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Thaduri, Adithya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Maintenance 4.0 in railway transportation industry2015Conference paper (Refereed)
    Abstract [en]

    Transportation systems are complex with respect to technology and operations with involvement in a wide range of human actors, organizations and technical solutions. For the operations and control of such complex environments, a viable solution is to apply intelligent computerized systems, such as computerized traffic control systems for coordinating airline transportation, or advanced monitoring and diagnostic systems in vehicles. Moreover, transportation assets cannot compromise the safety of the passengers by applying operation and maintenance activities. Indeed safety becomes a more difficult goal to achieve using traditional maintenance strategies and computerized solutions come into the picture as the only option to deal with complex systems interacting among them trying to balance the growth in technical complexity together with stable and acceptable dependability indexes. Industry 4.0 is a term that describes the fourth generation of industrial activity which is enabled by smart systems and Internet-based solutions. Two of the characteristic features of Industry 4.0 are computerization by utilizing cyber-physical systems and intelligent factories that are based on the concept of "internet of things". Maintenance is one of the application areas, referred to as maintenance 4.0, in form of self-learning and smart systems that predicts failure, makes diagnosis and triggers maintenance by making use of “internet of things”. This paper discusses the possibilities that lie within applying the maintenance 4.0 concept in the railway transportation industry. This paper also discusses the positive effects on technology; organisation and operations from a systems perspective.

  • 116.
    Kans, Mirka
    et al.
    Linnaeus University.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Thaduri, Adithya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Maintenance 4.0 in Railway Transportation Industry2016In: Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015) / [ed] Kari T. Koskinen; Helena Kortelainen; Jussi Aaltonen; Teuvo Uusitalo; Kari Komonen; Joseph Mathew; Jouko Laitinen, Encyclopedia of Global Archaeology/Springer Verlag, 2016, p. 317-331Conference paper (Refereed)
    Abstract [en]

    Transportation systems are complex with respect to technology and operations with involvement in a wide range of human actors, organisations and technical solutions. For the operations and control of such complex environments, a viable solution is to apply intelligent computerised systems, such as computerised traffic control systems for coordinating airline transportation, or advanced monitoring and diagnostic systems in vehicles. Moreover, transportation assets cannot compromise the safety of the passengers by applying operation and maintenance activities. Indeed safety becomes a more difficult goal to achieve using traditional maintenance strategies and computerised solutions come into the picture as the only option to deal with complex systems interacting among them trying to balance the growth in technical complexity together with stable and acceptable dependability indexes. Industry 4.0 is a term that describes the fourth generation of industrial activity which is enabled by smart systems and Internet-based solutions. Two of the characteristic features of Industry 4.0 are computerization by utilising cyber-physical systems and intelligent factories that are based on the concept of “internet of things”. Maintenance is one of the application areas, referred to as maintenance 4.0, in form of self-learning and smart systems that predicts failure, makes diagnosis and triggers maintenance by making use of “internet of things”. This paper discusses the possibilities that lie within applying the maintenance 4.0 concept in the railway transportation industry and the positive effects on technology, organisation and operations from a systems perspective.

  • 117.
    Kapur, P.K.
    et al.
    Amity University.
    Srividya, A.
    University College, Haugesund.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Foreword2013In: International Journal of Reliability, Quality and Safety Engineering (IJRQSE), ISSN 0218-5393, Vol. 20, no 3, p. 1-2Article in journal (Other academic)
  • 118.
    Karadimou, Eva
    et al.
    York EMC Services.
    Armstrong, Robert A.
    York EMC Services.
    Adin, Iñigo
    CEIT.
    Deniau, Virgine
    IFSTTAR.
    Rodriguez, Joseba P.
    CAF-ID.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Niska, Stefan
    Trafikverket.
    Tamarit, Jaime
    CEDEX, Centro de estudios y experimentación de obras públicas.
    An EMC study on the interopability of the European railway network2015In: IEEE International Symposium on Electromagnetic Compatibility (EMC), 2015 [joint conference with] EMC Europe: 16-22 Aug. 2015, Dresden, Piscataway, NJ: IEEE Communications Society, 2015, p. 428-433, article id 7256200Conference paper (Refereed)
    Abstract [en]

    The research presented here deals with the electromagnetic compatibility in the railway environment. In particular it focuses on four research areas: the spot signalling systems, the track circuits, the GSM-R and the broadcasting services. A review of the current railway standards is followed by a research on the immunity limits, the worst case scenarios and cross acceptance EMC tests for the four areas

  • 119.
    Karim, Ramin
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Dersin, Pierre
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Alstom Digital & Integrated Systems, St-Ouen, France.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Jarl, Håkan
    Tåg i Bergslagen, Borlänge, Sweden.
    AI Factory -- A Framework for Digital Asset Management2021In: Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021) / [ed] Bruno Castanier; Marko Cepin; David Bigaud; Christophe Berenguer, Research Publishing Services, 2021, p. 1160-1167Conference paper (Refereed)
    Abstract [en]

    Advanced analytics empowered by Artificial Intelligence (AI) contributes to the achievement of global sustainability and business goals. It will also contribute to global competitiveness of enterprises through enablement of fact-based decisionmaking and improved insight. The digitalisation process currently ongoing in industry, and the corresponding implementation of AI technologies, requires availability and accessibility of data and models. Data and models are considered as digital assets (ISO55K) that impact a system’s dependability during its whole lifecycle. Digitalisation and implementation of AI in complex technical systems such as found in railway, mining, and aerospace industries is challenging. From a digital asset management perspective, the main challenges can be related to source integration, content processing, and cybersecurity.

    However, to effectively and efficiently retain the required performance of a complex technical system during its lifecycle, there is a need of appropriate concepts, methodologies, and technologies. With this background, Luleå University of Technology, in cooperation with a number of Swedish railway stakeholders – fleet managers, railway undertakings, infrastructure managers and Original Equipment Manufacturers (OEM), has created a universal platform called ‘the AI Factory’ (AIF). The concept of AIF has further been specialised for railway industry, so called AI Factory for Railway (AIF/R).

    Hence, this paper aims to provide a description of findings from the development and implementation of ‘AI Factory (AIF)’ in the railway context. Furthermore, the paper provides a case-study description used to verify the developed technologies and methodologies within AIF/R.

  • 120.
    Karim, Ramin
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    AI Factory: Theories, Applications and Case Studies2023 (ed. 1)Book (Other academic)
    Abstract [en]

    This book provides insights into how to approach and utilise data science tools, technologies, and methodologies related to artificial intelligence (AI) in industrial contexts. It explains the essence of distributed computing and AI technologies and their interconnections. It includes descriptions of various technology and methodology approaches and their purpose and benefits when developing AI solutions in industrial contexts. In addition, this book summarises experiences from AI technology deployment projects from several industrial sectors. Features:

    • Presents a compendium of methodologies and technologies in industrial AI and digitalisation.

    • Illustrates the sensor-to-actuation approach showing the complete cycle, which defines and differentiates AI and digitalisation.

    • Covers a broad range of academic and industrial issues within the field of asset management.

    • Discusses the impact of Industry 4.0 in other sectors.

    • Includes a dedicated chapter on real-time case studies.

    This book is aimed at researchers and professionals in industrial and software engineering, network security, AI and machine learning (ML), engineering managers, operational and maintenance specialists, asset managers, and digital and AI manufacturing specialists.

  • 121.
    Karim, Ramin
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Westerberg, Jesper
    eMaintenance365 AB Aurorum 1C, SE-977 75, Luleå, Sweden.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Maintenance Analytics – The New Know in Maintenance2016In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 49, no 28, p. 214-219Article in journal (Refereed)
    Abstract [en]

    Decision-making in maintenance has to be augmented to instantly understand and efficiently act, i.e. the new know. The new know in maintenance needs to focus on two aspects of knowing: 1) what can be known and 2) what must be known, in order to enable the maintenance decision-makers to take appropriate actions. Hence, the purpose of this paper is to propose a concept for knowledge discovery in maintenance with focus on Big Data and analytics. The concept is called Maintenance Analytics (MA). MA focuses in the new knowledge discovery in maintenance. MA addresses the process of discovery, understanding, and communication of maintenance data from four time-related perspectives, i.e. 1) “Maintenance Descriptive Analytics (monitoring)”; 2) “Maintenance Diagnostic Analytics”; 3) “Maintenance Predictive Analytics”; and 4) “Maintenance Prescriptive analytics”.

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  • 122.
    Kumar, Uday
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Maintenance in the Era of Industry 4.0: Issues and Challenges2018In: Quality, IT and Business Operations: Modeling and Optimization / [ed] Kapur P., Kumar U., Verma A., Singapore: Springer, 2018, p. 231-250Chapter in book (Refereed)
    Abstract [en]

    The fourth generation of industrial activity enabled by smart systems and Internet-based solutions is known as Industry 4.0. Two most important characteristic features of Industry 4.0 are computerization using cyber-physical systems and the concept of “Internet of Things” adopted to produce intelligent factories. As more and more devices are instrumented, interconnected and automated to meet this vision, the strategic thinking of modern-day industry has been focused on deployment of maintenance technologies to ensure failure-free operation and delivery of services as planned.

    Maintenance is one of the application areas, referred to as Maintenance 4.0, in the form of self-learning and smart system that predicts failure, makes diagnosis and triggers maintenance. The paper addresses the new trends in manufacturing technology based on the capability of instrumentation, interconnection and intelligence together with the associated maintenance challenges in the era of collaborative machine community and big data environment.

    The paper briefly introduces the concept of Industry 4.0 and presents maintenance solutions aligned to the need of the next generation of manufacturing technologies and processes being deployed to realize the vision of Industry 4.0.The suggested maintenance approach to deal with new challenges due to the implementation of industry 4.0 is captured within the framework of eMaintenance solutions developed using maintenance analytics. The paper is exploratory in nature and is based on literature review and study of the current development in maintenance practices aligned to industry 4.0.

  • 123.
    Kumar, Uday
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Transformative Maintenance Technologies and Business Solutions for the Railway Assets2021In: Handbook of Advanced Performability Engineering / [ed] Krishna B. Misra, Springer Nature, 2021, p. 565-595Chapter in book (Other academic)
  • 124.
    Kumar, Uday
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Parida, Aditya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Stenström, Christer
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Maintenance audits using balanced scorecard and maturity model2011In: Maintworld, ISSN 1798-7024, E-ISSN 1799-8670, no 3, p. 34-40Article in journal (Other academic)
    Abstract [en]

    There is increasing interest in the use of maintenance performance measurement (MPM) and the possibility of using the maintenance audits for benchmarking metrics. This article proposes a methodology for simple measurement, one that accepts the indicators used on a scorecard with four perspectives and is hierarchized according to organizational level. The maintenance audit will evaluate the degree of fulfillment of objectives and the degree of satisfaction obtained from each of those perspectives. It will provide a clear picture of the current status of maintenance organization and the success of implemented policies taking into account the maintenance maturity model, i.e, the logical evolution of the maintenance function in the company.

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  • 125.
    Kumar, Uday
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Parida, Aditya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Stenström, Christer
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, L.
    University of Zaragoza.
    Maintenance performance metrics: a state of the art review2011In: MPMM 2011: Maintenance Performance Measurement & Management: Conference Proceedings / [ed] Diego Galar; Aditya Parida; Håkan Schunnesson; Uday Kumar, Luleå: Luleå tekniska universitet, 2011, p. 3-34Conference paper (Refereed)
    Abstract [en]

    This paper provides an overview of research and developments in the measurement of maintenance performance. It considers the problems of various measuring parameters and comments on the lack of structure in, and references for, the measurement of maintenance performance.The main focus is to determine how value can be created for organizations by measuring maintenance performance, looking at such maintenance strategies as condition based maintenance, reliability centered maintenance, e-maintenance etc. In other words, the objectives are to find frameworks or models that can be used to evaluate different maintenance strategies and determine the value of these frameworks for an organization.The paper asks the following research questions:- What approaches and techniques are used for Maintenance Performance Measurement (MPM) and which MPM techniques are optimal for evaluating maintenance strategies?- In general, how can MPM create value for organizations, and more specifically, which system of measurement is best for which maintenance strategy?The body of knowledge on maintenance performance is both quantitative and qualitative based. Quantitative approaches include economic and technical ratios, value-based and balanced scorecards, system audits, composite formulations, and statistical and partial maintenance productivity indices. Qualitative approaches include human factors, amongst others. Qualitative-based approaches are adopted because of the inherent limitations of effectively measuring a complex function such as maintenance through quantitative models. Maintenance decision makers often come to the best conclusion using heuristics, backed up by qualitative assessment, supported by quantitative measures. Both maintenance performance perspectives are included in this overview.

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  • 126.
    Kumar, Uday
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Parida, Aditya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Stenström, Christer
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    University of Zaragoza.
    Maintenance performance metrics: a state-of-the-art review2013In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 19, no 3, p. 233-277Article in journal (Refereed)
    Abstract [en]

    Purpose - This paper provides an overview of research and development in the measurement of maintenance performance. It considers the problems of various measuring parameters and comments on the lack of structure in and references for the measurement of maintenance performance. The main focus is to determine how value can be created for organizations by measuring maintenance performance, examining such maintenance strategies as condition-based maintenance, reliability-centred maintenance, e-maintenance, etc. In other words, the objectives are to find frameworks or models that can be used to evaluate different maintenance strategies and determine the value of these frameworks for an organization.Design/methodology/approach - A state-of-the-art literature review has been carried out to answer the following two research questions. Firstly, what approaches and techniques are used for maintenance performance measurement (MPM) and which MPM techniques are optimal for evaluating maintenance strategies? Secondly, in general, how can MPM create value for organizations and, more specifically, which system of measurement is best for which maintenance strategy?Findings - The body of knowledge on maintenance performance is both quantitatively and qualitatively based. Quantitative approaches include economic and technical ratios, value-based and balanced scorecards, system audits, composite formulations, and statistical and partial maintenance productivity indices. Qualitative approaches include human factors, amongst other aspects. Qualitatively based approaches are adopted because of the inherent limitations of effectively measuring a complex function such as maintenance through quantitative models. Maintenance decision makers often come to the best conclusion using heuristics, backed up by qualitative assessment, supported by quantitative measures. Both maintenance performance perspectives are included in this overview.Originality/value - A comprehensive review of maintenance performance metrics is offered, aiming to give, in a condensed form, an extensive introduction to MPM and a presentation of the state of the art in this field.

  • 127.
    Kumar, Uday
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Karim, Ramin
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kour, Ravdeep
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Editorial2024In: International Congress and Workshop on Industrial AI and eMaintenance 2023 / [ed] Kumar U.; Karim R.; Galar D.; Kour R., Springer Science and Business Media Deutschland GmbH , 2024, p. v-viChapter in book (Other academic)
  • 128.
    Kumar, Uday
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Karim, RaminLuleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.Galar, DiegoLuleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.Kour, RavdeepLuleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    International Congress and Workshop on Industrial AI and eMaintenance 20232024Conference proceedings (editor) (Refereed)
  • 129.
    Lamban, Pilar
    et al.
    Departamento de Ingeniería de Diseño y Fabricación, Universidad de Zaragoza, Spain.
    Royo, Jesús
    Departamento de Ingeniería de Diseño y Fabricación, Zaragoza Logístics Center, Spain.
    Valencia, Javier
    Departamento de Ingeniería de Diseño y Fabricación, Universidad de Zaragoza, Spain.
    Berges, Luis
    Departamento de Ingeniería de Diseño y Fabricación, Universidad de Zaragoza, Spain.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Modelo para el cálculo del costo de almacenamiento de un producto: Caso de estudio en un entorno logístico: [Model for calculating the storage cost of a product: Study case in a logistics environment]2013In: Dyna, ISSN 0012-7353, E-ISSN 2346-2183, Vol. 80, no 179, p. 23-32Article in journal (Refereed)
    Abstract [en]

    Several authors have established how important it is for companies to have accurate product cost information, especially in the actual environment of intense global competition. However, it has been shown that traditional systems do not satisfy these business demands, so in recent years new cost methods have been proposed, nevertheless these are still inaccurate. It is because of this situation that this paper presents a new methodology for determining the storage cost of a product that can be extrapolated to all the links of the supply chain. In turn, we propose a new cost driver, the logistics index, which helps to provide more precise information than traditional methods. It concludes by showing a business case where this model is implemented in a Spanish logistics company.

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    fulltext
  • 130.
    Lemma, Yonas
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Famurewa, Stephen Mayowa
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Schunnesson, Håkan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Fjellner, Jonas
    Boliden AB.
    CMMS benchmarking development in mining industries2012In: Proceedings of the 2nd International Workshop & Congress on eMaintenance: Dec 12-14 Luleå, Sweden : eMaintenace: trends in technologies and methodologies, challenges, possibilities and applications / [ed] Ramin Karim; Aditya Parida; Uday Kumar, Luleå: Luleå tekniska universitet, 2012, p. 211-218Conference paper (Refereed)
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  • 131.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Salgado, Oscar
    IK4-Ikerlan.
    Hybrid modelling in condition-based maintenance for smart assets2016Conference paper (Refereed)
    Abstract [en]

    When it comes to take proper maintenance decisions regarding reliability and safety of a system, there is a need to perform a right health assessment. Thus, acquiring signals from the system in healthy and damaged conditions gives the chance to analyse the effect of the state of the system on its response. However, it is usually hard to perform diagnosis and prognosis using only tests from the real system. The advances in technologies involving internet of things, cloud computing and big data lead to a situation in which this analysis of acquired data can be complemented by the use physics-based modelling. Thus, a combination of both data-driven and physics-based approaches can be implemented thanks to the aforementioned progress. In this paper an architecture to implement hybrid modelling is proposed, based on data fusion between real data and synthetic data obtained by simulations of a physics-based model. This architecture has two analysis levels: an online process carried out in a local basis and virtual commissioning performed in the cloud. The former results in failure detection analysis for avoiding upcoming failures whereas the latter has as aim a further analysis involving both diagnosis and prognosis.

  • 132.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.
    Mishra, Madhav
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Salgado, Oscar
    IK4-Ikerlan.
    Synthetic data generation in hybrid modelling of rolling element bearings2015In: Insight: Non-Destructive Testing & Condition Monitoring, ISSN 1354-2575, E-ISSN 1754-4904, Vol. 57, no 7, p. 395-400Article in journal (Refereed)
    Abstract [en]

    Diagnosis and prognosis processes are necessary to optimise the dependability of systems and ensure their safe operation. If there is a lack of information, faulty conditions cannot be identified and undesired events cannot be predicted. It is essential to predict such events and mitigate risks, but this is difficult in complex systems.Abnormal or unknown faults cause problems for maintenance decision makers. We therefore propose a methodology that fuses data-driven and model-based approaches. Real data acquired from a real system and synthetic data generated from a physical model can be used together to perform diagnosis and prognosis.As systems have time-varying conditions related to both the operating condi- tions and the healthy or faulty state of systems, the idea behind the proposed methodology is to generate synthetic data in the whole range of conditions in which a system can work. Thus, data related to the context in which the system is operating can be generated.We also take a first step towards implementing this methodology in the field of rolling element bearings. Synthetic data are generated using a physical model that reproduces the dynamics of these machine elements. Condition indicators such as root mean square, kurtosis and shape factor, among others, are calculated from the vibrational response of a bearing and merged with the real features obtained from the data collected from the functioning systemFinally, the merged indicators are used to train SVM classifiers (support vector machines), so that a classification according to the condition of the bearing is made independently of the applied loading conditions even though some of the scenarios have not yet occurred.

  • 133.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.
    Mishra, Madhav
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Modelo dinámico de rodamientos para su estudio frente a fallos geométricos locales2014Conference paper (Refereed)
  • 134.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.
    Mishra, Madhav
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Nonlinear response of rolling element bearings with local defects2014Conference paper (Refereed)
    Abstract [en]

    Rolling element bearings have been studied for decades, but more research is required into their dynamics, especially failure due to different kinds of damage in the context of condition monitoring. The appearance of a failure in an element of a bearing, as well as its degradation, can entail not only a malfunction in the system in which it is located, but also a catastrophic failure. This work presents a multi-body model of a rolling element bearing with the objective of analysing the dynamics of the bearing and emphasising the effect of defects in any of its element. The study models the metal-metal contacts between the bearing’s elements using the Hertz contact and the elastohydrodynamic lubricationtheories, both of which are theories of nonlinearity. It also considers the non-stationary regime of bearings and local geometric damage. Its results are compared with results in the literature. Finally, it includes a set of additional results showing different aspects of the response of the bearing.

  • 135.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan Technology Research Centre, Control and Monitoring Area.
    Salgado, Oscar
    IK4-Ikerlan Technology Research Centre, Control and Monitoring Area.
    Ciani, Lorenzo
    University of Florence, Department of Information Engineering.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Catelani, Marcantonio
    University of Florence, Department of Information Engineering.
    Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data2017In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 108, p. 152-162Article in journal (Refereed)
    Abstract [en]

    The advances in technology involving internet of things, cloud computing and big data mean a new perspective in the calculation of reliability, maintainability, availability and safety by combining physics-based modelling with data-driven modelling. This paper proposes an architecture to implement hybrid modelling based on the fusion of real data and synthetic data obtained in simulations using a physics-based model. This architecture has two levels of analysis: an online process carried out locally and virtual commissioning performed in the cloud. The former results in failure detection analysis to avoid upcoming failures whereas the latter leads to both diagnosis and prognosis. The proposed hybrid modelling architecture is validated in the field of rotating machinery using time-domain and frequency-domain analysis. A multi-body model and a semi-supervised learning algorithm are used to perform the hybrid modelling. The state of a rolling element bearing is analysed and accurate results for fault detection, localisation and quantification are obtained. The contextual information increases the accuracy of the results; the results obtained by the model can help improve maintenance decision making and production scheduling. Future work includes a prescriptive analysis approach.

  • 136.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Estimation of the reliability of rolling element bearings using a synthetic failure rate2015Conference paper (Refereed)
    Abstract [en]

    As rolling element bearings are key parts of rotating machinery, the estimation of their reliability is very important. In this context, different standards and research articles propose how to estimate fatigue life for different levels of reliability. However, when trying to do calculations based on data from a real system, there are many difficulties because of economic and safety reasons. Consequently, the use of physical models to simulate the cases that are difficult to reproduce in a real system allows us to generate synthetic data related to them. Thus, in this paper a synthetic failure rate of rolling element bearings is calculated using a physical modelling approach. A multi-body model of a bearing is used in order to obtain its dynamic response in non-stationary conditions and in different degradation levels. Thus, synthetic data are generated to cover a range of degradation related to geometric changes in the surface of the parts of the bearing. Some of the output variables of these synthetic data, such as vibration, are used as covariates of a proportional hazard model, which is then trained to estimate the reliability of the bearing. In this way, a synthetic failure rate is obtained in such a way that it can improve the failure rate given by the manufacturers.

  • 137.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Estimation of the Reliability of Rolling Element Bearings Using a Synthetic Failure Rate2016In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective, Springer International Publishing , 2016, 1, p. 99-112Chapter in book (Refereed)
    Abstract [en]

    As rolling element bearings are key parts of rotating machinery, the estimation of their reliability is very important. In this context, different standards and research articles propose how to estimate fatigue life for different levels of reliability. However, when trying to do calculations based on data from a real system, there are many difficulties because of economic and safety reasons. Consequently, the use of physical models to simulate the cases that are difficult to reproduce in a real system allows us to generate synthetic data related to them. Thus, in this paper a synthetic failure rate of rolling element bearings is calculated using a physical modelling approach. A multi-body model of a bearing is used in order to obtain its dynamic response in non-stationary conditions and in different degradation levels. Thus, synthetic data are generated to cover a range of degradation related to geometric changes in the surface of the parts of the bearing. Some of the output variables of these synthetic data, such as vibration, are used as covariates of a proportional hazard model, which is then trained to estimate the reliability of the bearing. In this way, a synthetic failure rate is obtained in such a way that it can improve the failure rate given by the manufacturers.

  • 138.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan Technology Research centre, Control and Monitoring Area, Arrasate-Mondragon, Spain.
    Salgado, Oscar
    IK4-Ikerlan Technology Research centre, Control and Monitoring Area, Arrasate-Mondragon, Spain.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Methodology for the physics-based modelling of multiple rolling element bearing configurations2017In: Proceedings of the Institution of mechanical engineers. Proceedings part K, journal of multi-body dynamics, ISSN 1464-4193, E-ISSN 2041-3068, Vol. 231, no 1, p. 194-212Article in journal (Refereed)
    Abstract [en]

    Condition-based maintenance is a maintenance strategy which can be employed for monitoring the condition of rolling element bearings (REBs). For that purpose, the physics-based modelling of these machine elements is an interesting approach. There is a wide range of REBs regarding their internal configuration, dimensions and operating conditions. In this paper, a methodology to create a physics-based mathematical model to reproduce the dynamics of multiple kinds of REB is presented. Following a multi-body modelling, the proposed methodology takes advantage of the reusability of models to cover a wide range of bearing configurations, as well as to generalise the dimensioning of the bearing and the application of the operating conditions. The methodology is proved to be valid by its application to two case studies. Simulations of a deep-groove ball bearing and a cylindrical roller bearing are carried out, analysing their dynamic response as well as analysing the effects of damage in their parts. Results of the two case studies show good agreement with experimental data and results of other models in literature.

  • 139.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Multi-body modelling of rolling element bearings and performance evaluation with localised damage2016In: Eksploatacja i Niezawodność – Maintenance and Reliability, ISSN 1507-2711, E-ISSN 2956-3860, Vol. 18, no 4, p. 638-648Article in journal (Refereed)
    Abstract [en]

    Condition-based maintenance is an extended maintenance approach for many systems, including rolling element bearings. Forthat purpose, the physics-based modelling of these machine elements is an interesting method. The use of rolling element bearingsis extended to many fields, what implies a variety of the configurations that they can take regarding the kind of rolling elements,the internal configuration and the number of rows. Moreover, the differences of the applications make rolling element bearingsto take different sizes and to be operating at different conditions regarding both speed and loads. In this work, a methodology tocreate a physics-based mathematical model to reproduce the dynamics of multiple kinds of rolling element bearings is presented.Following a multi-body modelling, the proposed strategy takes advantage of the reusability of models to cover a wide range ofbearing configurations, as well as to generalise the dimensioning of the bearing and the application of the operating conditions.Simulations of two bearing configurations are presented in this paper, analysing their dynamic response as well as analysing theeffects of damage in their parts. Results of the two case studies show good agreement with experimental data and results of othermodels in literature.

  • 140.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Test rig model development and validation for the diagnosis of rolling element bearings2016In: 14th IMEKO TC10 Workshop on Technical Diagnostics 2016: New Perspectives in Measurements, Tools and Techniques for Systems Reliability, Maintainability and Safety, Milan, Italy, 27-28 June 2016, 2016, p. 46-49Conference paper (Refereed)
    Abstract [en]

    In the context of condition based maintenance, carrying out diagnosis and prognosis processes is a key. For that purpose the evaluation of the condition of a machine is necessary, for which the development of physical models is useful as the response of the modelled system can be obtained in different operating conditions. In this paper, an electromechanical model for a rotary machine is presented, making special emphasis on the modelling of rolling element bearings. Thus, the response to different damaged conditions is evaluated. The proposed model is validated by comparing the simulation results with experimental signals acquired by tests carried out at different operating conditions. This comparison shows a good agreement as differences less than 0.6 % for the model of the bearing and differences up to the 10 % for the modelling of the rest of the elements are obtained.

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  • 141.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Validation of a physics-based model for rolling element bearings with diagnosis purposes2016In: Proceedings from the 8th European Workshop on Structural Health Monitoring (EWSHM 2016), 2016, Vol. 21, p. 461-470Conference paper (Refereed)
    Abstract [en]

    The use of rolling element bearings is widely extended to many fields such as wind energy systems, transportation and machine tools, among others. This broad use makes their performance analysis an interesting field of research. There are techniques to determine the life of a bearing and the on-going failure, if any, under some assumptions with some values of reliability. However, the unfulfilment of those hypothesis or other effects that affect the standard operation of rolling element bearings (e.g. current leakage, overloading, corrosion, etc.) leads to a higher probability of the appearance of failure. The monitoring of the condition of rolling element bearings has two main goals, the diagnosis and the prognosis of the item. Indeed, diagnosis, i.e. damage detection, localization and identification, has a great interest on the knowledge of the state of rolling element bearings in order to prevent faulty situations that may cause risky or costly situations, identifying those adverse situations and trying to mitigate the undesired effects.Therefore, risky situations due to failures need additional knowledge about the dynamics of a system (rolling element bearings in this case) and physics-based models can be used in order to represent it. They have an interesting potential due to the fact that they are able to simulate situations that may arise in some damaged conditions that might be either difficult, costly or insecure to reproduce in a real system. However, there is a need to validate the physics-based models to assure that it follows the real response of the system.This work presents the validation process of a model already developed by the authors. Experimental tests have been done in a test rig and the vibration measurements taken from these tests have been used to validate the model. Damage on the surface of the outer race has been induced to one of the rolling element bearings of the test rig. Thus, frequency-domain and order-domain analysis have been performed and the experimental results have been compared to the results obtained from the simulations. Differences lower than 2.5 % have been found for a wide range of constant and variable speeds and, hence, the model is validated.

  • 142.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan Technology Research Centre.
    Salgado, Oscar
    IK4-Ikerlan Technology Research Centre.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Validation of a physics-based model of a rotating machine for synthetic data generation in hybrid diagnosis2017In: Structural Health Monitoring, ISSN 1475-9217, E-ISSN 1741-3168, Vol. 16, no 4, p. 458-470Article in journal (Refereed)
    Abstract [en]

    Diagnosis and prognosis are key issues in the application of condition based maintenance. Thus, there is a need to evaluate the condition of a machine. Physics-based models are of great interest as they give the response of a modelled system in different operating conditions. This strategy allows for the generation of synthetic data that can be used in combination with real data acquired by sensors to improve maintenance. The article presents an electromechanical model for a rotating machine, with special emphasis on the modelling of rolling element bearings. The proposed model is validated by comparing the simulation results and the experimental results in different operating conditions and different damaged states. This comparison shows good agreement, obtaining differences of up to 10% for the modelling of the whole rotating machine and less than 0.6% for the model of the bearing.

  • 143.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Mishra, Madhav
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Methodology for the Estimation of the Fatigue Life of Rolling Element Bearings in Non-stationary Conditions2016In: Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the Fourth International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO'2014, Lyon, France December 15-17 / [ed] Fakher Chaari; Radozlaw Zimroz; Walter Bertelmus; Mahamed Haddar, Cham: Encyclopedia of Global Archaeology/Springer Verlag, 2016, Vol. 4, p. 413-423Conference paper (Refereed)
    Abstract [en]

    The estimation of the life of rolling element bearings (REBs) is crucial to determine when maintenance is required. This paper presents a methodology to calculate the fatigue life of REBs considering non-stationary conditions. Instead of taking a constant value, the paper considers cyclic loading and unloading processes, as well as increasing and decreasing values of the speed of rotation. It employs a model-based approach to calculate contact loads between the different elements of the bearing, with a finite element model (FEM) used to calculate the contact stresses. Using this information, it then performs a fatigue analysis to study overloading in faulty bearings.

  • 144.
    Linnéusson, Gary
    et al.
    School of Engineering Science, University of Skövde.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Wickelgren, Mikael
    School of Business, University of Skövde.
    In Need for Better Maintenance Cost Modelling to Support the Partnership with Manufacturing2016In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar; Alireza Ahmadi; Ajit Kumar Verma; Prabhakar Varde, Encyclopedia of Global Archaeology/Springer Verlag, 2016, p. 263-282Conference paper (Refereed)
    Abstract [en]

    The problem of maintenance consequential costs has to be dealt with in manufacturing and is core of this paper. The need of sustainable partnership between manufacturing and maintenance is addressed. Stuck in a best practice thinking, applying negotiation as a method based on power statements in the service level agreement, the common best possible achievable goal is put on risk. Instead, it may enforce narrow minded sub optimized thinking even though not intended so. Unfortunately, the state of origin is not straightforward business. Present maintenance cost modelling is approached, however limits to its ability to address the dynamic complexity of production flows are acknowledged. The practical problem to deal with is units put together in production flows; in which downtime in any unit may or may not result in decreased throughput depending on its set up. In this environment accounting consequential costs is a conundrum and a way forward is suggested. One major aspect in the matter is the inevitable need of shift in mind, from perspective thinking in maintenance and manufacturing respectively towards shared perspectives, nourishing an advantageous sustainable partnership.

  • 145.
    Lopes, Julio C.O.
    et al.
    Division of Mechanical Engineering, Technological Institute of Aeronautics, São José dos Campo.
    Scarpel, Rodrigo
    Division of Mechanical Engineering, Technological Institute of Aeronautics, São José dos Campo.
    Abrahão, Fernando T.M.
    Laboratory of Logistics Engineering, Technological Institute of Aeronautics, São José dos Campo.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ahmadi, Alireza
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Optmization In Performance-Based Logistics Contracts2017In: Proceedings of MPMM 2016: 6th International Conference on Maintenance Performance Measurement and Management, 28 November 2016, Luleå, Sweden / [ed] Diego Galar, Dammika Seneviratne, Luleå: Luleå tekniska universitet, 2017, p. 133-138Conference paper (Refereed)
    Abstract [en]

    Performance-based    Logistics    (PBL)    contracts    require  metrics  and  methodologies  set  in  a  systemic  way  to  provide  readiness  for  the  warfighter  at  reasonable  costs.  There  must be a clever and verifiable connection of metrics in order to access  and  analyze  data and  to  deliver  sound  and  consistent  inputs  for  the  entire  support  system  to  accomplish  its  goals.    A  system   of   incentives   and   penalties   usually   takes   place   and   suggests  functions  to  be  maximized  and/or  minimized  in  a  multi-criterion  and  highly  integrated  environment.  This  study  deals  with  the  optimization  of  the  entire  setup  of  Performance-Based Logistics  contracts  and  is  limited  to  a  preliminary  study  in  a  simple  scenario  that  has  showed  a  promises  results.    A  mixed  method, using both the Ɛ-Constraint  and    Goal  Programming  is  proposed to model the case. Results indicate that metrics used for reliability,    maintainability,    availability    and    supportability    (RAMS)  and  costs  can  be  optimized  simultaneously  for  clever  contracts.

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  • 146.
    Lopes, Julio C.O.
    et al.
    Division of Mechanical Engineering, Technological Institute of Aeronautics, São José dos Campo.
    Scarpel, Rodrigo
    Division of Mechanical Engineering, Technological Institute of Aeronautics, São José dos Campo.
    Abrahão, Fernando T.M.
    Laboratory of Logistics Engineering, Technological Institute of Aeronautics, São José dos Campo.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ciani, Lorenzo
    University of Florence, Department of Information Engineering.
    Optimization in performance-based logistics contracts2017In: 4th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2017: proceedings, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 413-418, article id 7999608Conference paper (Refereed)
    Abstract [en]

    Performance-based Logistics (PBL) contracts require metrics and methodologies set in a systemic way to provide readiness for the warfighter at reasonable costs. There must be a clever and verifiable connection of metrics in order to access and analyze data and to deliver sound and consistent inputs for the entire support system to accomplish its goals. A system of incentives and penalties usually takes place and suggests functions to be maximized and/or minimized in a multi-criterion and highly integrated environment. This study deals with the optimization of the entire setup of Performance-Based Logistics contracts and is limited to a preliminary study in a simple scenario that has showed a promises results. A mixed method, using both the £-Constraint and Goal Programming is proposed to model the case. Results indicate that metrics used for reliability, maintainability, availability and supportability (RAMS) and costs can be optimized simultaneously for clever contracts.

  • 147.
    Mishra, Madhav
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Leturiondo, Urko
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Synthetic data for hybrid prognosis2014In: Proceedings of the European Conference of the Prognostics and Health Management Society 2014, 2014, p. 796-801Conference paper (Refereed)
    Abstract [en]

    Using condition-based maintenance (CBM) to assess machinery health is a popular technique in many industries, especially those using rotating machines. CBM is relevant in environments where the prediction of a failure and the prevention and mitigation of its consequences increase both profit and safety. Prognosis is the most critical part of this process and the estimation of Remaining Useful Life (RUL) is essential once failure is identified. This paper presents a method of synthetic data generation for hybrid model-based prognosis. In this approach, physical and data-driven models are combined to relate process features to damage accumulation in time-varying service equipment. It uses parametric models and observer-based approaches to Fault Detection and Identification (FDI). A nominal set of parameters is chosen for the simulated system, and a sensitivity analysis is performed using a general-purpose simulation package. Synthetic data sets are then generated to compensate for information missing in the acquired data sets. Information fusion techniques areproposed to merge real and synthetic data to create training data sets which reproduce all identified failure modes, even those that do not occur in the asset, such as Reliability Centered Maintenance (RCM), Failure Mode and Effect Analysis(FMEA). This new technology can lead to better prediction of remaining useful life of rotating machinery and minimizing and mitigating the costly effects of unplanned maintenance actions.

  • 148.
    Mishra, Madhav
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Leturiondo-Zubizarreta, Urko
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-IKERLAN. Mechanical Engineering. J. M. Arizmendiarrieta 2 -20500 Arrasate-Mondragón, Gipuzkoa, Spain.
    Salgado-Picón, Óscar
    IK4-IKERLAN. Mechanical Engineering. J. M. Arizmendiarrieta 2 -20500 Arrasate-Mondragón, Gipuzkoa, Spain.
    Galar-Pascual, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Hybrid modelling for failure diagnosis and prognosis in the transport sector. Acquired data and synthetic data: [Modelización híbrida para el diagnóstico y pronóstico de fallos en el sector del transporte. Datos adquiridos y datos sintéticos]2015In: Dyna, ISSN 0012-7361, Vol. 90, no 2, p. 139-145Article in journal (Refereed)
    Abstract [en]

    Safety in transport is a key. Railway and aerospace sectors have a need for ways to predict the behaviour of trains and aircraft, respectively. With this information, maintenance tasks for the correct operation of the assets can be carried out, reducing the number of failures that can cause an accident. However, the lack of enough data of the faulty state of those systems makes this to be difficult. Because of that either hidden faults or unknown faults can occur. As regulations in transport are very restrictive, components are usually substituted in early states of their degradation, which implies a loss of useful life of those components.In this article a methodology to overcome this limitation is presented. This methodology consists in the fusion of data obtained from two sources: data acquired from the real system, and synthetic data generated using physical models of the system. These physical models should be constructed in such a way that they can reproduce the main failure modes that can occur in the modelled system. This data fusion, that creates a hybrid model, not only allows to classify the condition of the system according to the aforementioned failure modes, but also to define new data that do not belong to any of those failure modes as a new failure mode, improving diagnosis and prognosis processes.

  • 149.
    Mishra, Madhav
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Saari, Juhamatti
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Leturiondo, Urko
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Hybrid Models for Rotating Machinery Diagnosis and Prognosis: Estimation of Remaining Useful Life2014Report (Other academic)
    Abstract [en]

    The purpose of this literature review is to summarise the various technologies that can be used for machinery diagnosis and prognosis. The review focuses on Condition Based Maintenance (CBM) in machinery systems, with a short description of the theory behind each technology; it also includes references to state-of-the-art research into each theory. When we compare technologies, especially with respect to cost, complexity, and robustness, we find varied abilities across technologies. The machinery health assessment for CBM deployment is accepted worldwide; it is very popular in industries using rotating machines involved. These techniques are relevant in environments where predicting a failure and preventing or mitigating its consequences will increase both profit and safety. Prognosis is the most critical part of this process and is now recognised as a key feature in maintenance strategies; the estimation of Remaining Useful Life (RUL) is essential when a failure is identified. The literature review identifies three basic ways to model the fault development process: with symbols, data, or mathematical formulations based on physical principles. The review discusses hybrid approaches to machinery diagnosis and prognosis; it notes some typical approaches and discusses their advantages and disadvantages.

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  • 150.
    Morant, Amparo
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Tamarit, Jaime
    CEDEX, Centro de estudios y experimentación de obras públicas.
    Cloud computing for maintenance of railway signalling systems2012In: Proceedings of The Ninth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2012, 2012, Vol. 1, p. 551-559Conference paper (Refereed)
    Abstract [en]

    Signalling systems in railway allow the control, supervision and protection of railway traffic. These systems play an important part in a railway’s capacity and availability. Thus, their reliability and maintenance are key concerns. A number of signalling systems are on the market today; these work to guarantee safety while meeting the required capacity of the network. In order to keep the railway network in an optimal state, it is critical for the signalling systems to have tools that can make data mining and analysis easier and faster. The solution described herein allows data mining and posterior analysis without depending on the elements that provide the data. This is a key factor for signalling systems, due to their complexity and continuous development. For integration purposes, a data collection and distribution system based on the concept of cloud computing is proposed to collect data or information pertaining to the assets of the railway signalling systems. From a maintenance point of view, a benefit is that information or data may be collected pertaining to the health, variability, performance or utilization of an asset.

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    FULLTEXT01
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