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  • 1.
    Ahmadi, Alireza
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gupta, Suprakash
    Banaras Hindu University, Varanasi.
    Ghodrati, Behzad
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Estimation of economic consequences of aircraft system failures2012Ingår i: Communications in Dependability and Quality Management, ISSN 1450-7196, Vol. 15, nr 1, s. 39-49Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A large portion of the direct and indirect aircraft operational costs stems from the consequences of decisions made during the maintenance program development. Decision on maintenance task selection for non-safety category of failures, is based on the cost effectiveness, in which the cost of preventive maintenance should be less than the costs associated with the corrective action and failure consequence. Although the assessment of the direct cost for preventive and corrective maintenance is quiet straightforward, however quantification and estimation of the cost associated with the consequence of failure is a great challenge. This is due to a long list of contributory factors and lack of adequate data regarding the cost headings. This study attempts to estimate the economic consequences of aircraft system failures which lead to a technical delay. The paper considers financial losses, mostly due to the additional unexpected costs related to the flight crew, passengers, aircraft itself, ramp and airport, when one of the cost headings, e.g. the pre-fixed crew cost is known. The experience of the field experts has been used following a pairwise comparison technique to compare the cost headings, and to estimate the contribution of each one to the total cost of a delay. The study shows that the proposed model can be a tool to assess the cost of failure consequences in aircraft operation, when there is a limited data and information regarding the cost headings.

  • 2.
    Aminu Sanda, Mohammed
    et al.
    Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle.
    Abrahamsson, Lena
    Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Arbetsvetenskap.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Sandin, Fredrik
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Delsing, Jerker
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Lean instrumentation framework for sensor pruning and optimization in condition monitoring2011Ingår i: The Eighth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies: St. David's Hotel, Cardiff, Wales, 20 - 22 June 2011 ; CM2011/MFPT2011, Longborough, Glos: Coxmoor Publishing Co. , 2011, Vol. 1, s. 202-215Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper discusses a lean instrumentation framework for guiding the introduction of the lean concept in condition monitoring in order to enhance the organizational capability (i.e. human, technical and management trichotomy) and reduce the complexity in the maintenance management systems of industrial companies. Additionally, decision-making, based on severity diagnosis and prognosis in condition monitoring, is a complex maintenance function which is based on large data-set of sensors measurements. Yet, the entirety of such decision-making is not dependent on only the sensors measurements, but also on other important indices, such as the human factors, organizational aspects and knowledge management. This is because, the ability to identify significant features from large amount of measured data is a major challenge for automated defect diagnosis, a situation that necessitate the need to identify signal transformations and features in new domains. The need for the lean instrumentation framework is justified by the desire to have a modern condition monitoring system with the capability of pruning to the optimal level the number of sensors required for efficient and effective serviceability of the maintenance process. It is concluded that there are methodologies that can be developed to enable more efficient condition monitoring systems, with benefits for many processes along the value chain.

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  • 3.
    Baglee, David
    et al.
    Institute for Automotive Manufacturing and Advanced Practices, University of Sunderland, Department of Computing, Engineering and Technology, Institute for Automotive and Manufacturing Advanced Practise, University of Sunderland, School of Computing and Technology, University of Sunderland.
    Knowles, Michael
    Institute for Automotive Manufacturing and Advanced Practices, University of Sunderland, University of Sunderland.
    Kinnunen, Sini Kaisu
    School of Business and Management, Lappeenranta University of Technology.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    A proposed maintenance strategy for a wind turbine gearbox using condition monitoring techniques2016Ingår i: International Journal of Process Management and Benchmarking, ISSN 1460-6739, E-ISSN 1741-816X, Vol. 6, nr 3, s. 386-403Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Renewable energy sources such as wind are available without limitations, but reliability is critical if pay back periods are to be met. The current reliability and failure modes of offshore wind turbines are known and have been used to develop preventive and corrective maintenance strategies but have done little to improve reliability. The analysis of gear lubricants can detect early signs of failure. Reliability centred maintenance (RCM) approach offers considerable benefit to the management of wind turbine operation, as it includes an appreciation of the impact of faults. This paper provides an overview of the application of RCM and condition monitoring techniques, to support the development of a maintenance strategy. It discusses the development of a sensor-based processing unit that can continuously monitor the lubricated systems and provide, real-time data enabling onshore staff to predict degradation anticipate problems and take remedial action before damage and failure occur

  • 4.
    Baglee, David
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Knowles, Michael
    University of Sunderland.
    Morris, Adrian
    University of Sunderland.
    O´Hagan, Geraldine
    Glenmorangie Company.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Optimisation of food and engineering supply chain technology (OPTFEST): a case study2013Ingår i: International Congress of Condition Monitoring and Diagnostic Engineering Management: Comadem 2013 / [ed] Antti Heijo, Helsinki: KP-Media Oy Messuaukio 1 00520 Helsinki Finland , 2013, s. 498-503Konferensbidrag (Refereegranskat)
    Abstract [en]

    Predictive maintenance attempts to detect theonset of a degradation mechanism with thegoal of correcting that degradation prior tosignificant deterioration in the component orequipment. The diagnostic capabilities ofpredictive maintenance technologies haveincreased in recent years. The advances insensor technologies, component sensitivities,size reductions, and most importantly, cost,has allowed manufacturing processes,especially where once this technology was‘missing’, the opportunity to enter a new andnecessary area of diagnostics. One area inparticular is the food and drink industry.However, with the introduction of any newtechnology, proper application and training isof critical importance. In addition, theimplementation of any new maintenancestrategy should be supported by a welldeveloped information system. This paper willpresent the development and implementation,through case study analysis, of a newmaintenance strategy using predictivemaintenance strategies and an informationsystem designed to support staff training. Thisproject has resulted in the transfer of modernmaintenance technologies, alreadysuccessfully implemented in other industrysectors to the food processing sector. This hasbeen achieved through the transfer andimplementation of structured maintenancemethods and the introduction of monitoringtools for processing equipment. Significantbenefits include the ability to predict equipmentfailure, the development of best practice andcompliance with supplier audits. Theinformation interchange systems developed inthe project allow both users and suppliers todevelop and improve engineering andmaintenance guidelines, thus enabling theuser to improve plant and production efficiencyand determine the correct mix of technologies.

  • 5.
    Berges, Luis
    et al.
    Department of Design Engineering and Manufacturing, University of Zaragoza.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Stenström, Christer
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Qualitative and quantitative aspects of maintenance performance measurement: a data fusion approach2013Ingår i: International Journal of Strategic Engineering Asset Management (IJSEAM), ISSN 1759-9733, E-ISSN 1759-9741, Vol. 1, nr 3, s. 238-252Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The measurement of maintenance performance is often faced with a lack in knowledge about the real function of the maintenance department within organisations, and consequently appropriate targets from the global mission and vision are absence. Measurement metrics are not adapted to real needs, which have a strong human factor; nor is there a roadmap of the amount of data to be collected, their processing or how they are used in decision making. This article proposes a model where qualitative and quantitative methods are combined to complement the advantages of both.

  • 6.
    Björling, Sten-Erik
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Baglee, David
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Singh, Sarbjeet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Maintenance knowledge management with fusion of CMMS and CM2013Ingår i: DMIN 2013 International Conference on Data Mining: 22nd -25th July 2013, Las Vegas, Nevada, USA, 2013Konferensbidrag (Refereegranskat)
    Abstract [en]

    Maintenance can be considered as an information, knowledge processing and management system. The management of knowledge resources in maintenance is a relatively new issue compared to Computerized Maintenance Management Systems (CMMS) and Condition Monitoring (CM) approaches and systems. Information Communication technologies (ICT) systems including CMMS, CM and enterprise administrative systems amongst others are effective in supplying data and in some cases information. In order to be effective the availability of high-quality knowledge, skills and expertise are needed for effective analysis and decision-making based on the supplied information and data. Information and data are not by themselves enough, knowledge, experience and skills are the key factors when maximizing the usability of the collected data and information. Thus, effective knowledge management (KM) is growing in importance, especially in advanced processes and management of advanced and expensive assets. Therefore efforts to successfully integrate maintenance knowledge management processes with accurate information from CMMSs and CM systems will be vital due to the increasing complexities of the overall systems.Low maintenance effectiveness costs money and resources since normal and stable production cannot be upheld and maintained over time, lowered maintenance effectiveness can have a substantial impact on the organizations ability to obtain stable flows of income and control costs in the overall process. Ineffective maintenance is often dependent on faulty decisions, mistakes due to lack of experience and lack of functional systems for effective information exchange [10]. Thus, access to knowledge, experience and skills resources in combination with functional collaboration structures can be regarded as vital components for a high maintenance effectiveness solution.Maintenance effectiveness depends in part on the quality, timeliness, accuracy and completeness of information related to machine degradation state, based on which decisions are made. Maintenance effectiveness, to a large extent, also depends on the quality of the knowledge of the managers and maintenance operators and the effectiveness of the internal & external collaborative environments. With emergence of intelligent sensors to measure and monitor the health state of the component and gradual implementation of ICT) in organizations, the conceptualization and implementation of E-Maintenance is turning into a reality. Unfortunately, even though knowledge management aspects are important in maintenance, the integration of KM aspects has still to find its place in E-Maintenance and in the overall information flows of larger-scale maintenance solutions. Nowadays, two main systems are implemented in most maintenance departments: Firstly, Computer Maintenance Management Systems (CMMS), the core of traditional maintenance record-keeping practices that often facilitate the usage of textual descriptions of faults and actions performed on an asset. Secondly, condition monitoring systems (CMS). Recently developed (CMS) are capable of directly monitoring asset components parameters; however, attempts to link observed CMMS events to CM sensor measurements have been limited in their approach and scalability. In this article we present one approach for addressing this challenge. We argue that understanding the requirements and constraints in conjunction - from maintenance, knowledge management and ICT perspectives - is necessary. We identify the issues that need be addressed for achieving successful integration of such disparate data types and processes (also integrating knowledge management into the “data types” and processes).

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  • 7.
    Catelani, Marcantonio
    et al.
    Department of Information Engineering, University of Florence, via di S. Marta 3, 50139, Florence, Italy.
    Ciani, Lorenzo
    Department of Information Engineering, University of Florence, via di S. Marta 3, 50139, Florence, Italy.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Industry and Transport Division, Tecnalia Research and Innovation, Miñano, Araba , 01510, Spain.
    Guidi, Giulia
    Department of Information Engineering, University of Florence, via di S. Marta 3, 50139, Florence, Italy.
    Matucci, Serena
    Department of Mathematics and Computer Science “Ulisse Dini”, University of Florence, Florence, Italy.
    Patrizi, Gabriele
    Department of Information Engineering, University of Florence, via di S. Marta 3, 50139, Florence, Italy.
    FMECA assessment for railway safety-critical systems investigating a new risk threshold method2021Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 9, s. 86243-86253Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper develops a Failure Mode, Effects and Criticality Analysis (FMECA) for a heating, ventilation and air conditioning (HVAC) system in railway. HVAC is a safety critical system which must ensure emergency ventilation in case of fire and in case of loss of primary ventilation functions. A study of the HVAC’s critical areas is mandatory to optimize its reliability and availability and consequently to guarantee a low operation and maintenance cost. The first part of the paper describes the FMECA which is performed and reported to highlight the main criticalities of the HVAC system under analysis. Secondly, the paper deals with the problem of the evaluation of a threshold risk value, which can distinguish negligible and critical failure modes. Literature barely considers the problem of an objective risk threshold estimation. Therefore, a new analytical method based on finite difference is introduced to find a univocal risk threshold value. The method is then tested on two Risk Priority Number datasets related to the same HVAC. The threshold obtained in both cases is a good tradeoff between the risk mitigation and the cost investment for the corrective actions required to mitigate the risk level. Finally, the threshold obtained with the proposed method is compared with the methods available in literature. The comparison shows that the proposed finite difference method is a well-structured technique, with a low computational cost. Furthermore, the proposed approach provides results in line with the literature, but it completely deletes the problem of subjectivity.

  • 8.
    Catelani, Marcantonio
    et al.
    Department of Information Engineering, University of Florence, Florence, 50139, Italy.
    Ciani, Lorenzo
    Department of Information Engineering, University of Florence, Florence, 50139, Italy.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Industry and Transport Division, Tecnalia Research and Innovation, Miñano, 01510, Spain.
    Patrizi, Gabriele
    Department of Information Engineering, University of Florence, Florence, 50139, Italy.
    Optimizing Maintenance Policies for a Yaw System Using Reliability-Centered Maintenance and Data-Driven Condition Monitoring2020Ingår i: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 69, nr 9, s. 6241-6249Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    System downtime and unplanned outages massively affect plant productivity; therefore, the reliability, availability, maintainability, and safety (RAMS) disciplines, together with fault diagnosis and condition monitoring (CM), are mandatory in energy applications. This article focuses on the optimization of a maintenance plan for the yaw system used in an onshore wind turbine (WT). A complete reliability-centered maintenance (RCM) procedure is applied to the system to identify which maintenance action is the optimal solution in terms of cost, safety, and availability. The scope of the research is to propose a new customized decision-making diagram inside the RCM assessment to reduce the subjectivity of the procedure proposed in the standard and save the cost by optimizing maintenance decisions, making the projects more cost-efficient and cost-effective. This article concludes by proposing a new diagnostic method based on a data-driven CM system to efficiently monitor the health and detect damages in the WT by means of measurements of critical parameters of the tested system. This article highlights how a reliability analysis, during the early phase of the design, is a very helpful and powerful means to guide the maintenance decision and the data-driven CM.

  • 9.
    Catelani, Marcantonio
    et al.
    Department of Information Engineering, University of Florence, Florence, Italy.
    Ciani, Lorenzo
    Department of Information Engineering, University of Florence, Florence, Italy.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Patrizi, Gabriele
    Department of Information Engineering, University of Florence, Florence, Italy.
    Risk Assessment of a Wind Turbine: A New FMECA-Based tool with RPN threshold estimation2020Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 8, s. 20181-20190Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A wind turbine is a complex system used to convert the kinetic energy of the wind into electrical energy. During the turbine design phase, a risk assessment is mandatory to reduce the machine downtime and the Operation & Maintenance cost and to ensure service continuity. This paper proposes a procedure based on Failure Modes, Effects, and Criticality Analysis to take into account every possible criticality that could lead to a turbine shutdown. Currently, a standard procedure to be applied for evaluation of the risk priority number threshold is still not available. Trying to fill this need, this paper proposes a new approach for the Risk Priority Number (RPN) prioritization based on a statistical analysis and compares the proposed method with the only three quantitative prioritization techniques found in literature. The proposed procedure was applied to the electrical and electronic components included in a Spanish 2 MW on-shore wind turbine.

  • 10.
    Catelani, Marcantonio
    et al.
    Department of information engineering, University of Florence via di S. Marta 3, 50139, Florence (Italy).
    Ciani, Lorenzo
    Department of information engineering, University of Florence, via di S. Marta 3, Florence, 50139, Italy.
    Guidi, Giulia
    Department of information engineering, University of Florence, via di S. Marta 3, Florence, 50139, Italy.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    A Practical Solution for HVAC Life Estimation Using Failure Models2020Ingår i: 17th IMEKO TC 10 and EUROLAB Virtual Conference: "Global Trends in Testing, Diagnostics and Inspection for 2030" / [ed] Zsolt János Viharos; Lorenzo Ciani; Piotr Bilski; Mladen Jakovcic, International Measurement Confederation (IMEKO) , 2020, s. 85-91Konferensbidrag (Refereegranskat)
    Abstract [en]

    Heating, ventilation, and air conditioning (HVAC) is the technology of indoor and vehicular environmental comfort. The objectives of HVAC systems are to provide an acceptable level of occupancy comfort and process function, to maintain good indoor air quality, and to keep system costs and energy requirements to a minimum. Performing a reliability prediction provides an awareness of potential equipment degradation during the equipment life cycle. Reliability under a range of conditions is one of the most important requirements to guarantee in HVAC installed on trains. Predicting the reliability of mechanical equipment requires the consideration of its exposure to the environment and subjection to a wide range of stress levels such as impact loading. Often analysist find an unavailability of failure data in handbooks and problems for acquiring data for mechanical components, so the mentioned problems demonstrates the need for reliability prediction models. The paper deals with a HVAC installed on a high-speed train and evaluates the failure rates through the failure rate models suggested by the handbooks in order to assess a model which includes all the mechanical parts.

  • 11.
    Catelani, Marcantonio
    et al.
    Department of information engineering, University of Florence via di S. Marta 3, 50139, Florence, Italy.
    Ciani, Lorenzo
    Department of information engineering, University of Florence via di S. Marta 3, 50139, Florence, Italy.
    Guidi, Giulia
    Department of information engineering, University of Florence via di S. Marta 3, 50139, Florence, Italy.
    Patrizi, Gabriele
    Department of information engineering, University of Florence via di S. Marta 3, 50139, Florence, Italy.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Estimate the useful life for a heating, ventilation, and air conditioning system on a high-speed train using failure models2021Ingår i: Acta IMEKO, ISSN 0237-028X, Vol. 10, nr 3, s. 100-107Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Heating, ventilation, and air conditioning (HVAC) is a widely used system used to guarantee an acceptable level of occupancy comfort, to maintain good indoor air quality, and to minimize system costs and energy requirements. If failure data coming from company database are not available, then a reliability prediction based on failure rate model and handbook data must be carried out. Performing a reliability prediction provides an awareness of potential equipment degradation during the equipment life cycle. Otherwise, if field data regarding the component failures are available, then classical reliability assessment techniques such as Fault Tree Analysis and Reliability Block Diagram should be carried out. Reliability prediction of mechanical components is a challenging task that must be carefully assessed during the design of a system. For these reasons, this paper deals with the reliability assessment of an HVAC using both failure rate model for mechanical components and field data. The reliability obtained using the field data is compared to the one achieved using the failure rate models in order to assess a model which includes all the mechanical parts. The study highlights how it is fundamental to analyze the reliability of complex system integrating both field data and mathematical model.

  • 12.
    Catelani, Marcantonio
    et al.
    Dept of Information Engineering University of Florence, Italy.
    Ciani, Lorenzo
    Dept of Information Engineering University of Florence, Italy.
    Patrizi, Gabriele
    Dept of Information Engineering University of Florence, Italy.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Tecnalia Research and Innovation, Spain .
    Reliability improvement of wind turbine control system based on standby redundancy2019Ingår i: ISSE 2019 - 5th IEEE International Symposium on Systems Engineering: 2019 Symposium Proceedings, IEEE, 2019, artikel-id 8984510Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    Reliability analysis is widely used in many industrial fields to predict the remaining life of complex systems by assessing their current health status. This paper deals with one of the best-known techniques for reliability analysis: the reliability block diagram. This method models the reliability of the system based on the system's architecture and the reliability of its components. The work analyses the control system of a 2MW wind turbine, proposing two different reliability models. The first draws on a standard control system architecture. The second introduces a cold standby redundancy architecture for the data acquisition subsystem and a warm standby redundancy architecture for the power supply subsystem. With these configurations, it is possible to improve the system reliability by neglecting some failure modes because one of the branches of the redundant configuration will be either inactive or partially active.

  • 13.
    Ciani, L.
    et al.
    Department of information Engineering, University of Florence, via di S. Marta 3, Florence, 50139, Italy.
    Guidi, G.
    Department of information Engineering, University of Florence, via di S. Marta 3, Florence, 50139, Italy.
    Patrizi, G.
    Department of information Engineering, University of Florence, via di S. Marta 3, Florence, 50139, Italy.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Industry and Transport Division, Tecnalia Research and Innovation, Miñano, 01510, Spain.
    Condition-Based Maintenance of HVAC on a High-Speed Train for Fault Detection2021Ingår i: Electronics, E-ISSN 2079-9292, Vol. 10, nr 12, artikel-id 1418Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Reliability-centered maintenance (RCM) is a well-established method for preventive maintenance planning. This paper focuses on the optimization of a maintenance plan for an HVAC (heating, ventilation and air conditioning) system located on high-speed trains. The first steps of the RCM procedure help in identifying the most critical items of the system in terms of safety and availability by means of a failure modes and effects analysis. Then, RMC proposes the optimal maintenance tasks for each item making up the system. However, the decision-making diagram that leads to the maintenance choice is extremely generic, with a consequent high subjectivity in the task selection. This paper proposes a new fuzzy-based decision-making diagram to minimize the subjectivity of the task choice and preserve the cost-efficiency of the procedure. It uses a case from the railway industry to illustrate the suggested approach, but the procedure could be easily applied to different industrial and technological fields. The results of the proposed fuzzy approach highlight the importance of an accurate diagnostics (with an overall 86% of the task as diagnostic-based maintenance) and condition monitoring strategy (covering 54% of the tasks) to optimize the maintenance plan and to minimize the system availability. The findings show that the framework strongly mitigates the issues related to the classical RCM procedure, notably the high subjectivity of experts. It lays the groundwork for a general fuzzy-based reliability-centered maintenance method. 

  • 14.
    Ciani, Lorenzo
    et al.
    Univ Florence, Dept Informat Engn, Via S Marta 3, I-50139 Florence, Italy.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Tecnalia Res & Innovat, Ind & Transport Div, Minano Araba 01510, Spain.
    Patrizi, Gabriele
    Univ Florence, Dept Informat Engn, Via S Marta 3, I-50139 Florence, Italy.
    Improving context awareness reliability estimation for a wind turbine using an RBD model2019Ingår i: 2019 IEEE International Instrumentation and Measurement Technology conference (I2MTC), New York: IEEE, 2019, s. 245-250Konferensbidrag (Refereegranskat)
    Abstract [en]

    All devices are fabricated from materials that degrade with time. Degradation will continue until some critical device parameter can no longer meet the required specification for proper device functionality. Reliability estimation can assess the current health of a system and predict its remaining life. This kind of analysis is critical to improve safety, optimize scheduled maintenance, reduce life-cycle cost and minimize down time. This work analyses the reliability of a 2NIW wind turbine using the Reliability Block Diagram method. The paper compares the reliability estimated in standard environmental conditions and the reliability evaluated considering the real temperature and humidity values acquired using a SCADA system.

  • 15.
    Ciani, Lorenzo
    et al.
    Dept of Information Engineering, University of Florence, via di S. Marta 3, 50139, Florence, Italy.
    Guidi, Giulia
    Dept of Information Engineering, University of Florence, via di S. Marta 3, 50139, Florence, Italy.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Industry and Transport Division, Tecnalia Research and Innovation, Miñano (Araba), 01510, Spain.
    Reliability evaluation of an HVAC ventilation system with FTA and RBD analysis2020Ingår i: 2020 International Symposium on Systems Engineering (ISSE) Proceedings, IEEE, 2020Konferensbidrag (Refereegranskat)
    Abstract [en]

    Rail industry is rapidly developing, and rail becomes ever more viable in a wide range of regions. Therefore, the passenger experience and comfort has become a major concern for operators in the world. Heating, ventilation and air conditioning systems are used in railways to provide passengers thermal comfort and proper air motion. The ventilation system is one of the main elements of the system. Its components include both mechanical and electronic devices. All components are subjected to stress, and this tends to reduce their life cycle; the reliability of the ventilation system must be evaluated to plan and schedule appropriate maintenance activities. The paper evaluates reliability of the ventilation system using fault tree analysis and a reliability block diagram. Both techniques analyse data qualitatively; moreover, with specific algorithms they also provide quantitative results in term of reliability and probability of system failure. The paper compares the two reliability evaluation methods to verify their accuracy.

  • 16.
    Ciani, Lorenzo
    et al.
    Department of Information Engineering, University of Florence, via di Santa Marta 3, 50139, Florence, Italy.
    Guidi, Giulia
    Department of Information Engineering, University of Florence, via di Santa Marta 3, 50139, Florence, Italy.
    Patrizi, Gabriele
    Department of Information Engineering, University of Florence, via di Santa Marta 3, 50139, Florence, Italy.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Industry and Transport Division, Tecnalia Research and Innovation, Miñano (Araba), 01510, Spain.
    Improving Human Reliability Analysis for railway systems using fuzzy logic2021Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 9, s. 128648-128662Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The International Union of Railway provides an annually safety report highlighting that human factor is one of the main causes of railway accidents every year. Consequently, the study of human reliability is fundamental, and it must be included within a complete reliability assessment for every railway-related system. However, currently RARA (Railway Action Reliability Assessment) is the only approach available in literature that considers human task specifically customized for railway applications. The main disadvantages of RARA are the impact of expert’s subjectivity and the difficulty of a numerical assessment for the model parameters in absence of an exhaustive error and accident database. This manuscript introduces an innovative fuzzy method for the assessment of human factor in safety-critical systems for railway applications to address the problems highlighted above. Fuzzy logic allows to simplify the assessment of the model parameters by means of linguistic variables more resemblant to human cognitive process. Moreover, it deals with uncertain and incomplete data much better than classical deterministic approach and it minimizes the subjectivity of the analyst evaluation. The output of the proposed algorithm is the result of a fuzzy interval arithmetic, α-cut theory and centroid defuzzification procedure. The proposed method has been applied to the human operations carried out on a railway signaling system. Four human tasks and two scenarios have been simulated to analyze the performance of the proposed algorithm. Finally, the results of the method are compared with the classical RARA procedure underline compliant results obtain with a simpler, less complex and more intuitive approach.

  • 17.
    Darbari, Jyoti D.
    et al.
    Department of Operational Research, University of Delhi, India.
    Agarwal, Vernika
    Department of Operational Research, University of Delhi, India.
    Yadavalli, Venkata S.S.
    Department of Industrial and Systems Engineering, University of Pretoria, South Africa.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Jha, Prakash C.
    Department of Operational Research, University of Delhi, India.
    A multi-objective fuzzy mathematical approach for sustainable reverse supply chain configuration2017Ingår i: Journal of Transport and Supply Chain Management, ISSN 2310-8789, E-ISSN 1995-5235, Vol. 11, artikel-id a267Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Designing and implementation of reverse logistics (RL) network which meets the sustainability targets have been a matter of emerging concern for the electronics companies in India.

    Objectives: The present study developed a two-phase model for configuration of sustainable RL network design for an Indian manufacturing company to manage its end-of-life and endof-use electronic products. The notable feature of the model was the evaluation of facilities under financial, environmental and social considerations and integration of the facility selection decisions with the network design.

    Method: In the first phase, an integrated Analytical Hierarchical Process Complex Proportional Assessment methodology was used for the evaluation of the alternative locations in terms of their degree of utility, which in turn was based on the three dimensions of sustainability. In the second phase, the RL network was configured as a bi-objective programming problem, and fuzzy optimisation approach was utilised for obtaining a properly efficient solution to the problem.

    Results: The compromised solution attained by the proposed fuzzy model demonstrated that the cost differential for choosing recovery facilities with better environmental and social performance was not significant; therefore, Indian manufacturers must not compromise on the sustainability aspects for facility location decisions.

    Conclusion: The results reaffirmed that the bi-objective fuzzy decision-making model can serve as a decision tool for the Indian manufacturers in designing a sustainable RL network. The multi-objective optimisation model captured a reasonable trade-off between the fuzzy goals of minimising the cost of the RL network and maximising the sustainable performance of the facilities chosen.

  • 18.
    D'Emilia, G.
    et al.
    University of l'Aquila, L'Aquila, Italy.
    Gaspari, A.
    University of l'Aquila, L'Aquila, Italy.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Improvement of measurement contribution for asset characterization in complex engineering systems by an iterative methodology2018Ingår i: International Journal of Service Science, Management, Engineering, and Technology, ISSN 1947-959X, Vol. 9, nr 2, s. 85-103, artikel-id 4Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The evolution of systems based on the integration of Internet of Things (IoT) and Cloud computing technologies requires resolute and trustable management approaches, to let the industrial assets thrive and avoid losses in efficiency and, thus, profitability. In this article, a methodology based on the evaluation of the measurement uncertainty is proposed, which is able to suggest possible improvement paths and reliable decisions. The approach is based on the identification of subsequent tasks that should be fulfilled, also in a recursive way. Its application in the field, for the identification of the vibration and acoustic emission signatures of highly-performance machining tools, allows directing future actions to increase the potentiality of proper management of the information provided by measurements. In a complex scenario, characterized by many devices and instruments, the compliance with the procedures for measurement accuracy has proven to be a useful support.

  • 19.
    Diez-Olivan, Alberto
    et al.
    TECNALIA, Donostia-San Sebastián, Spain.
    Del Ser, Javier
    TECNALIA, Donostia-San Sebastián, Spain. Department of Communications Engineering, University of the Basque Country, Bilbao, Spain. Basque Center for Applied Mathematics (BCAM), Bilbao, Bizkaia, Spain.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. TECNALIA, Donostia-San Sebastián, Spain.
    Sierra, Basilio
    Department of Computer Sciences and Artificial Intelligence, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain.
    Data Fusion and Machine Learning for Industrial Prognosis: Trends and Perspectives towards Industry 4.02018Ingår i: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 50, s. 92-111Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The so-called “smartization” of manufacturing industries has been conceived as the fourth industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and progressive maturity of new Information and Communication Technologies (ICT) applied to industrial processes and products. From a data science perspective, this paradigm shift allows extracting relevant knowledge from monitored assets through the adoption of intelligent monitoring and data fusion strategies, as well as by the application of machine learning and optimization methods. One of the main goals of data science in this context is to effectively predict abnormal behaviors in industrial machinery, tools and processes so as to anticipate critical events and damage, eventually causing important economical losses and safety issues. In this context, data-driven prognosis is gradually gaining attention in different industrial sectors. This paper provides a comprehensive survey of the recent developments in data fusion and machine learning for industrial prognosis, placing an emphasis on the identification of research trends, niches of opportunity and unexplored challenges. To this end, a principled categorization of the utilized feature extraction techniques and machine learning methods will be provided on the basis of its intended purpose: analyze what caused the failure (descriptive), determine when the monitored asset will fail (predictive) or decide what to do so as to minimize its impact on the industry at hand (prescriptive). This threefold analysis, along with a discussion on its hardware and software implications, intends to serve as a stepping stone for future researchers and practitioners to join the community investigating on this vibrant field.

  • 20.
    Diez-Olivan, Alberto
    et al.
    TECNALIA, Basque Research and Technology Alliance (BRTA), 20009 Donostia-San Sebastién, Spain; University of the Basque Country (UPV/EHU), 20018 Donostia-San Sebastián, Spain.
    Ortego, Patxi
    TECNALIA, Basque Research and Technology Alliance (BRTA), 20009 Donostia-San Sebastién, Spain; University of the Basque Country (UPV/EHU), 20018 Donostia-San Sebastián, Spain.
    Del Ser, Javier
    TECNALIA, Basque Research and Technology Alliance (BRTA), 20009 Donostia-San Sebastién, Spain; University of the Basque Country (UPV/EHU), 20018 Donostia-San Sebastián, Spain.
    Landa-Torres, Itziar
    Petronor Innovación S.L., 48550 Muskiz, Spain.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Bizkaia, Spain.
    Camacho, David
    Technical University of Madrid, 28040 Madrid, Spain.
    Sierra, Basilio
    University of the Basque Country (UPV/EHU), 20018 Donostia-San Sebastián, Spain.
    Adaptive Dendritic Cell-Deep Learning Approach for Industrial Prognosis Under Changing Conditions2021Ingår i: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 17, nr 11, s. 7760-7770Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Industrial prognosis refers to the prediction of failures of an industrial asset based on data collected by Internet of Things sensors. Prognostic models can experience the undesired effects of concept drift, namely, the presence of nonstationary phenomena that affects the data collected over time. Consequently, fault patterns learned from data become obsolete. To overcome this issue, contextual and operational changes must be detected and managed, triggering rapid model adaptation mechanisms. This article presents an adaptive learning approach based on a dendritic cell algorithm for drift detection and a deep neural network model that dynamically adapts to new operational conditions. A kernel density estimator with drift-based bandwidth is used to generate synthetic data for a faster adaptation, focusing on fine-tuning the lowest neural layers. Experimental results over a real-world industrial problem shed light on the outperforming behavior of the proposed approach when compared to other drift detectors and classification models.

  • 21.
    Ding, X.
    et al.
    State Key Laboratory of Mechanical Transmission, Shazheng Street 174, Shapingba District, Chongqing, 400044, China.
    Shao, Y.
    State Key Laboratory of Mechanical Transmission, Shazheng Street 174, Shapingba District, Chongqing, 400044, China.
    He, Q.
    Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230026, China.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    A Subspace Clustering Chart Using a Reference Model for Featureless Bearing Performance Degradation Assessment2018Ingår i: MFPT 2018 - Intelligent Technologies for Equipment and Human Performance Monitoring, Proceedings, Society for Machinery Failure Prevention Technology , 2018, s. 35-49Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    The health index (HI) of machine condition must be sensitive and robust in complex working conditions. A systematic HI will assess machine performance automatically, reliably, and in a timely manner without intervention. This paper proposes a subspace clustering HI in a model using reference data on component health. Unlike the conventional HIs empirically learned from raw feature sets, a subspace clustering HI aims to automatically describe the migration and variation of the condition clustering distribution in a series of two-class subspace models derived from the raw data. First, in a featureless process, a covariance-driven Hankel matrix is directly constructed from the raw time-domain signal, and principal component analysis is used to separate the feature subspace and noise null-space. Second, in the index construction process, the reference health subspace data (from healthy data) and the monitored subspace data (from monitored data) are combined to construct a referenced model. Thus, a new spatial clustering HI with kernel operation is implemented to assess the current bearing performance and reveal discriminative features. The effectiveness of the proposed subspace clustering HI for the detection of abnormal condition is evaluated experimentally on bearing test-beds, using a mobile mapping mode. A novel subspace clustering chart, CUSUM-based spatial clustering HI, is developed to depict the real bearing performance degradation. Compared to the regular HI (e.g., root mean square), the proposed approach provides a more accurate and reliable degradation assessment profile with an early fault occurrence alarm. The experimental results show the potential of the proposed spatial clustering analysis to assess bearing degradation.

  • 22.
    Famurewa, Stephen Mayowa
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Asplund, Matthias
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Implementation of performance based maintenance contracting in railway industries2013Ingår i: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 4, nr 3, s. 231-240Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The achievement of maintenance objectives to support the overall business objectives is the pursuit of any maintenance department. Using in-house or outsourced maintenance service provider is a decision which poses challenge in the management of maintenance function. Should the decision be for outsourcing, the next concern is the selection of the most appropriate strategy suitable for the business environment, structure and philosophy. In an effort to improve maintenance function so as to deliver set objectives, some infrastructure managers (IM) adopted the approach of outsourcing maintenance function, giving larger responsibilities to maintenance service providers called contractors. Moreover, such change requires adequate attention to meet the pressing need of achieving the designed capacity of the existing railway infrastructure and also support a competitive and sustainable transport system. This paper discusses performance based railway infrastructure maintenance contracting with its issues and challenges. The approach of this article is review of literature and as well as synthesis of practices. A framework to facilitate the successful implementation of Performance Based Railway Infrastructure Maintenance (PBRIM) is presented. Also a performance monitoring system is proposed to assess the outcome and identify improvement potentials of the maintenance outsourcing strategy. A case study is given to demonstrate the monitoring of a typical maintenance activity that can be outsourced using this outsourcing strategy.

  • 23.
    Famurewa, Stephen Mayowa
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Stenström, Christer
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Asplund, Matthias
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Composite indicator for railway infrastructure management2014Ingår i: Journal of Modern Transportation, ISSN 2095-087X, E-ISSN 2196-0577, Vol. 22, nr 4, s. 214-224Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The assessment and analysis of railway infrastructure capacity is an essential task in railway infrastructure management carried out to meet the required quality and capacity demand of railway transport. For sustainable and dependable infrastructure management, it is important to assess railway capacity limitation from the point of view of infrastructure performance. However, the existence of numerous performance indicators often leads to diffused information that is not in a format suitable to support decision making. In this paper, we demonstrated the use of fuzzy inference system for aggregating selected railway infrastructure performance indicators to relate maintenance function to capacity situation. The selected indicators consider the safety, comfort, punctuality and reliability aspects of railway infrastructure performance. The resulting composite indicator gives a reliable quantification of the health condition or integrity of railway lines. A case study of the assessment of overall infrastructure performance which is an indication of capacity limitation is presented using indicator data between 2010 and 2012 for five lines on the network of Trafikverket (Swedish Transport Administration). The results are presented using customised performance dashboard for enhanced visualisation, quick understanding and relevant comparison of infrastructure conditions for strategic management. This gives additional information on capacity status and limitation from maintenance management perspective.

  • 24.
    Famurewa, Stephen Mayowa
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Stenström, Christer
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Asplund, Matthias
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Composite indicator for railway infrastructure management2013Konferensbidrag (Refereegranskat)
    Abstract [en]

    The assessment of efficiency and effectiveness of past maintenance decisions and actions is an essential element in maintenance process. The significance of this is not only limited to communicating the value contribution of maintenance to overall business objectives but also to drive maintenance for improvement and towards excellence. However the existence of numerous maintenance result areas and many operational level indicators often lead to distributed information that is not in a suitable format required to support decision making. This paper motivates the use of fuzzy logic approach to aggregate selected indicators to appreciate the information bit distributed in each indicator. The selected indicators include measures related to safety, comfort, punctuality, availability and reliability aspects of maintenance. Linguistic description and fuzzy sets are developed for each of the indicators which are regarded as input parameters. Also domain experts are employed to develop inference rules for the aggregation process. The methodology of using fuzzy inference system for aggregating maintenance performance indicators is demonstrated with selected line sections of Trafikverket (Swedish Transport Administration). The resulting composite indicator gives a reliable quantification of the health condition of the asset and performance of maintenance within the period under consideration. This can be easily communicated and benchmarked within the organization of the infrastructure manager.

  • 25.
    Farinha, José Manuel Torres
    et al.
    CEMUC® – University of Coimbra's Mechanical Engineering Research Center, University of Coimbra, Coimbra, Portugal.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Fonseca, Inácio Adelino
    CEMUC® – University of Coimbra's Mechanical Engineering Research Center, University of Coimbra, Coimbra, Portugal.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Certification of maintenance providers: a competitive advantage2013Ingår i: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 19, nr 2, s. 144-156Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Purpose – The purpose of this paper is to synthesize some relevant norms, namely European norms (EN), to the maintenance field. Design/methodology/approach – The methodology is based on the conjunction of the most relevant norms to the maintenance field that represent a coherent set of tools to aid maintenance activity and maintenance companies to achieve a new level of competitiveness. Findings – Until now, the companies have not given relevance to specific certifications, such as PAS 55 or NP4492. But, with the increase of competitiveness and the market more and more exigent, it is necessary to introduce this new paradigm to raise the maintenance activity at an upper level. Practical implications – The approach presented in the paper constitutes a base for an upper level of competitiveness among companies, based on common standards that make the maintenance activity more exigent and transparent. Originality/value – The paper presents a conjunction among standards, including the newest ones, that constitutes a new vision for maintenance providers, representing a definitive contribution for a new positioning of the maintenance market.

  • 26.
    Fornlöf, Veronica
    et al.
    University of Skövde.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Syberfeldt, Anna
    University of Skövde.
    Almgren, torgny
    GKN Aerospace Engine Systems, Trollhättan.
    On-Condition Parts Versus Life Limited Parts: A Trade off in Aircraft Engines2016Ingår i: 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, s. 253-262Konferensbidrag (Refereegranskat)
    Abstract [en]

    Maintaining an aircraft engine is both complex and time consuming since an aircraft is an advanced system with high demands on safety and reliability. Each maintenance occasion must be as effective as possible and the maintenance need to be executed without performing excessive maintenance. The aim of this paper is to describe the essence of aircraft engine maintenance and to point out the potential for improvement within the maintenance planning by improving the remaining life predictions of the On-Condition parts, i.e. parts that are not given a fixed life limit.

  • 27.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    A comparative study of artificial neural networks and support vector machine for fault diagnosis2013Ingår i: International Journal of Performability Engineering, ISSN 0973-1318, Vol. 9, nr 1, s. 49-60Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Fault detection is a crucial step in condition based maintenance requiring. The importance of fault diagnosis necessitates an efficient and effective failure pattern identification method. Artificial Neural Networks (ANN) and Support Vector Machines (SVM) emerging as prospective pattern recognition techniques in fault diagnosis have been showing its adaptability, flexibility and efficiency. Regardless of variants of the two techniques, this paper discusses the principle of the two techniques, and discusses their theoretical similarity and difference. Eventually using the commonest ANN, SVM, a case study is presented for fault diagnosis using a wide used bearing data. Their performances are compared in terms of accuracy, computational cost and stability

  • 28.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    An adaptive multiple kernel method-based support vector machine used for classication2013Ingår i: International Journal of Condition Monitoring, E-ISSN 2047-6426, Vol. 3, nr 1, s. 8-15Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Classification is an important technique used for condition monitoring. Extensive research has been carried out on classification and numerous techniques have been developed. The support vector machine (SVM) is one of these techniques; it has excellent classification capacity and is widely used. The effectiveness of the SVM depends on the selection of the kernel function, so to maximise performance this paper proposes using an adaptive multiple kernel SVM (AMK-SVM). Using AMK, many heterogeneous features, such as continuous, categorical, logical etc, can be merged. Instead of predefining the parameters of kernel functions as with other multiple kernel SVMs, this method can adapt its parameters to data automatically through kernel alignment. The paper offers two numerical examples: one with benchmarking data to test the feasibility and performance of the approach (for this case a two-layer neural network and two single kernel SVMs are applied to the same datasets to compare their performance with the AMK-SVM); the other example uses the AMK-SVM to discriminate a healthy bearing from a defective bearing

  • 29.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Failure detection using support vector machine and artificial neural networks: a comparative study2011Ingår i: The Eighth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies: St. David's Hotel, Cardiff, Wales, 20 - 22 June 2011 ; CM2011/MFPT2011, Longborough, Glos.: Coxmoor Publishing Co. , 2011, Vol. 1, s. 189-201Konferensbidrag (Refereegranskat)
  • 30.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Failure diagnosis of railway assets using support vector machine and ant colony optimization method2012Ingår i: International Journal of COMADEM, ISSN 1363-7681, Vol. 15, nr 2, s. 3-10Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Support Vector Machine (SVM) is an excellent technique for pattern recognition. This paper uses a multi-class SVM as a classifier to solve a multi-class classification problem for fault diagnosis. As the pre-defined parameters in the SVM influence the performance of the classification, this paper uses the heuristic Ant Colony Optimization (ACO) algorithm to find the optimal parameters. This multi-class SVM and ACO are applied to the fault diagnosis of an electric motor used in a railway system. A case study illustrates how efficient the ACO is in finding the optimal parameters. By using the optimal parameters from the ACO, the accuracy of the performed diagnosis on the electric motor is found to be highest.

  • 31.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser.
    Reliability prediction using support vector regression2010Ingår i: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 1, nr 3, s. 263-268Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Reliability prediction of machinery is crucial to schedule overhauls, spare parts replacement and maintenance interventions. Many AI tools have been used in order to provide these predictions for the industry. Support vector regression (SVR) is a nonlinear regression technique extended from support vector machine. SVR can fit data flexibly and it has a wide variety of applications. This paper utilizes SVR combining time series to predict the next failure time based on historical failure data. To solve the parameter selection problem a method has been proposed. This method approximates the widely used leave-one-out method. To bound the prediction error, a confidence interval is proposed based on the non-homogeneous poisson process. A numerical case from the mining industry is presented to demonstrate the developed approach.

  • 32.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Editorial Special Issue on Developments and Applications in Maintenance Performance2013Ingår i: International Journal of Strategic Engineering Asset Management (IJSEAM), ISSN 1759-9733, E-ISSN 1759-9741, Vol. 1, nr 3, s. 225-227Artikel i tidskrift (Övrigt vetenskapligt)
    Ladda ner fulltext (pdf)
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  • 33.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Reliability and maintenance programs in nuclear power plants2011Konferensbidrag (Refereegranskat)
  • 34.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Berges, Luis
    University of Zaragoza.
    Application of dynamic benchmarking of rotating machinery for eMaintenance2010Ingår i: Proceedings of the 1st international workshop and congress on eMaintenance, Luleå tekniska universitet, 2010, s. 227-233Konferensbidrag (Refereegranskat)
    Abstract [en]

    The vibration analysis and condition monitoring technology is based on comparison of measurements obtained with benchmarks suggested by manufacturers or standards. In this case, the references provided by current rules are static and independent of parameters such as age or environmental conditions in which the machine is analyzed.New communication technologies allow the integration of eMaintenance systems, production and real-time data or the result of vibration routes. The integration of all these data allows Data mining and extraction of parameters to be incorporated into decision-making typical of CBM, such as repairs, downtime, overhauls etc.This paper proposes the use of indicators that result from data mining as a reference dynamic, not static as proposed by the standard. The application of these references to the decision making process of the maintenance manager avoids unnecessary repairs caused by false alarms and thus prolongs the life of the equipment, resulting in the improvement of parameters such as the MTBF, in a eMaintenance system.

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  • 35. Galar, Diego
    et al.
    Berges, Muro, L.
    Department of Engineering Design and Manufacture, School of Engineering, University of Zaragoza.
    Royo, Javier
    Manufacturing Engineering and Advanced Metrology Group, Aragon Institute of Engineering Research (13A), University of Zaragoza.
    RAMS parameters as KPI rotating machinery maintenance2010Ingår i: COMADEM 2010: advances in maintenance and condition diagnosis technologies towards sustainable society : proceedings of the 23rd international congress on condition monitoring and diagnostic engineering management, June 28-July2, 2010, Nara, Japan / [ed] Susumu Okumura, Hikone: Sunrise Publishing , 2010, s. 851-858Konferensbidrag (Refereegranskat)
    Abstract [en]

    The implementation of a performance metrics in the maintenance function requires the development of indicators associated with it. These indicators have, as a main component, RAMS parameters derived from physical assets. The addition of information such as costs or organizational factors sets indicators for different hierarchical levels. The article illustrates two centrifugal pumps with the development of RAMS parameters and the proposed indicators at all developed hierarchical levels based on these parameters. The need for alignment of all maintenance KPIs, based on the primitive components are RAMS parameters, with corporate objectives mark the development of metrics and the underlying problems in their implementation.

  • 36.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Berges-Muro, Luis
    UniZar, Instituto de Educación Secundaria Corona de Aragón, Zaragoza, Spain.
    Lambán-Castillo, Pilar
    UniZar, Instituto de Educación Secundaria Corona de Aragón, Zaragoza, Spain.
    Huertas-Talón, Jose Luís
    UniZar, Instituto de Educación Secundaria Corona de Aragón, Zaragoza, Spain.
    Tormos-Martínez, Bernardo
    Universitat Politècnica de València (UPV), Spain.
    Cálculo de la vida útil remanente mediante trayectorias móviles entre hiperplanos de máquinas de de soporte vectorial: [Rul prediction using moving trajectories between svm hyper planes]2013Ingår i: Interciencia, ISSN 0378-1844, Vol. 38, nr 8, s. 556-562Artikel i tidskrift (Refereegranskat)
    Abstract [es]

    Se propone un nuevo método de predicción de vida útil remanente (RUL) inspirado en clasificadores de máquinas de soporte vectorial (SVM). Los datos históricos de condición de un sistema durante su tiempo de vida se utilizan para crear una clasificación mediante hiperplanos en SVM. Para estimar la RUL de un sistema, la velocidad de degradación se evalúa calculando la distancia mínima definida con base en las trayectorias de degradación; es decir, el acercamiento del sistema al hiperplano que segrega información de las condiciones buenas y malas en diferentes horizontes de tiempo. Se puede estimar la vida final de un componente específico, o la información de la RUL de una población ser calculada, mediante la agregación de múltiples estimaciones RUL usando un método de estimación de densidad. La degradación de un sistema se ve afectado por muchos factores desconocidos que, además de complicar los comportamientos de degradación, dificultan la recolección de datos con calidad. Debido a falta de conocimiento y medidas incompletas, normalmente se carece de información importante del contexto de los datos recogidos. Por ello se agrupan datos históricos del sistema con gran variedad de patrones de degradación, con los que la búsqueda de un modelo global depredicción RUL es extremadamente difícil. Esto lleva a buscar técnicas avanzadas de predicción más allá de los modelos tradicionales. El modelo propuesto desarrolla un método eficaz de predicción RUL que aborda múltiples retos en pronósticos de sistemas complejos. Las similitudes entre trayectorias de degradación pueden contrastarse para enriquecer las metodologías actuales de prognosis. Para verificar el modelo se emplean datos del monitorizado de condición en rodamientos.

  • 37.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Fuqing, Yuan
    Department of Engineering and Safety, University of Tromsø, Tromsø, Norway.
    Harmonic and Inter-harmonic Analysis on Power Signal from Railway Traction Systems2017Ingår i: International Journal of COMADEM, ISSN 1363-7681, Vol. 20, nr 2, s. 3-10Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A thorough investigation of wave velocity effects to the accuracy of damage location in a two dimensional source location algorithm of acoustic emission technique was carried out with pencil lead breaks (PLB) tests on a steel plate (SS400). Several AE signal propagation modes were investigated along with the experimental averaging values of wave velocity. Results show that the appropriate consideration of velocity mode in damage location is an important factor in reducing the errors of damage source location in acoustic emission technique.

  • 38.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ghodrati, Behzad
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Fusion of CMMS Data and CM Data - a real need for maintenance: Part 22012Ingår i: Maintworld, ISSN 1798-7024, E-ISSN 1799-8670, nr 3, s. 40-43Artikel i tidskrift (Övrigt vetenskapligt)
  • 39.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ghodrati, Behzad
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Fusion of CMMS data and CM data: a real need for maintenance (part I)2012Ingår i: Maintworld, ISSN 1798-7024, E-ISSN 1799-8670, nr 2, s. 42-45Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    Maintenance can be considered as an information processing system. Therefore, thedevelopment of future maintenance information systems is one of the most importantcurrent research problems to model the effects of automatic condition monitoringsystems enabled by embedded electronics and software.

  • 40.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Gustafson, Anna
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Tormos, Bernardo
    CMT - Motores Termicos, Uiversitat Politècnica de València, Valencia, Spain.
    Berges, Luis
    Department Design Engineering and Manufacturing, University of Zaragoza, Zaragoza, Spain.
    Maintenance Decision Making based on different types of data fusion: [Podejmowanie decyzji eksploatacyjnych w oparciu o fuzję różnego typu danych]2012Ingår i: Eksploatacja i Niezawodność – Maintenance and Reliability, ISSN 1507-2711, E-ISSN 2956-3860, Vol. 14, nr 2, s. 135-144Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Over the last decade, system integration is applied more as it allows organizations to streamline business processes. A recent development in the asset engineering management is to leverage the investment already made in process control systems. This allows the operations, maintenance, and process control teams to monitor and determine new alarm level based on the physical condition data of the critical machines. Condition-based maintenance (CBM) is a maintenance philosophy based on this massive data collection, wherein equipment repair or replacement decisions depend on the current and projected future health of the equipment. Since, past research has been dominated by condition monitoring techniques for specific applications; the maintenance community lacks a generic CBM implementation method based on data mining of such vast amount of collected data. The methodology would be relevant across different domains. It is necessary to integrate Condition Monitoring (CM) data with management data from CMMS (Computer Maintenance Management Systems) which contains information, such as: component failures, failure information related data, servicing or repairs, and inventory control and so on. These systems are the core of traditional scheduled maintenance practices and rely on bulk observations from historical data to make modifications to regulated maintenance actions. The most obvious obstacle in the integration of CMMS, process and CM data is the disparate nature of the data types involved, and there have benn several attempts to remedy this problem. Although, there have been many recent efforts to collect and maintain large repositories of these types of data, there have been relatively few studies to identify the ways these to datasets could be related. This paper attempts to fulfill that need by proposing a combined data mining-based methodology for CBM considering CM data and Historical Maintenance Management data. It shows a system integration of physical and management data that also supports business intelligence and data mining where data sets can be combined in non-traditional ways.

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  • 41.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Johansson, Carl-Anders
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Mouzoune, A.
    Mohammed V University.
    Taibi, S.
    Mohammed V University.
    Risk Based Maintenance policies for SMART devices2013Ingår i: Proceedings of International Conference Life Cycle Engineering and Management ICDQM-2013 / [ed] Ljubisa Papic, Prijevor: Research Center of Dependability and Quality Management DQM , 2013, Vol. 2, s. 470-486Konferensbidrag (Refereegranskat)
  • 42.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Johansson, Carl-Anders
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Mouzoune, A.
    Ecole Mohammadia d'ingénieurs, Mohammed V University – Agdal, Rabat.
    Taibi, S.
    Ecole Mohammadia d'ingénieurs, Mohammed V University – Agdal, Rabat.
    Risk Based Maintenance policies for SMART devices2013Ingår i: Communications in Dependability and Quality Management, ISSN 1450-7196, Vol. 16, nr 3, s. 15-28Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Maintenance activities are commonly organized into scheduled and unscheduled actions. Scheduled maintenance is undertaken during pre-programmed inspections. Such maintenance operations try to minimize the risk of deterioration based on a priori knowledge of failure mechanisms and their timing. However, in complex systems it is not always possible to schedule maintenance actions to mitigate all undesired effects, and SMART systems, which monitor selected parameters, propose actions to correct any deviation in normal behavior. Maintenance decisions must be made on the basis of accepted risk. Performed or not performed scheduled tasks as well as deferred corrective actions can have positive or negative consequences for the company, technicians and machines. These three risks should be properly assessed and prioritized as a function of the goals to be achieved. This paper focuses on how best practices in risk assessment for human safety can be successfully transferred to risk assessment for asset integrity.

  • 43.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kans, Mirka
    Department of Mechanical Engineering, Linnaeus University, Växjö.
    The Impact of Maintenance 4.0 and Big Data Analytics within Strategic Asset Management2017Ingår i: 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, s. 96-104Konferensbidrag (Refereegranskat)
    Abstract [en]

    The latest industrial revolution is manifested by smart and networking equipment. Realizing the full value of these machineries, and other business assets, has become increasingly important. Strategic asset management faces managerial, technical as well as methodological challenges, of which some could be reduced or overcome by applying technological solutions such as Internet of things, cloud computing, cyber-physical systems and big data analytics. This paper outlines the impact of the emerging technologies in the area of strategic management with special emphasis on the analytics as service provider for the maintenance functions.

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  • 44.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kans, Mirka
    Linnaeus University.
    Schmidt, Bernard
    University of Skövde.
    Big Data in Asset Management: Knowledge Discovery in Asset Data by the Means of Data Mining2016Ingår i: 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, s. 161-171Konferensbidrag (Refereegranskat)
    Abstract [en]

    Assets are complex mixes of complex systems, built from components which, over time, may fail. The ability to quickly and efficiently determine the cause of failures and propose optimum maintenance decisions, while minimizing the need for human intervention is necessary. Thus, for complex assets, much information needs to be captured and mined to assess the overall condition of the whole system. Therefore the integration of asset information is required to get an accurate health assessment of the whole system, and determine the probability of a shutdown or slowdown. Moreover, the data collected are not only huge but often dispersed across independent systems that are difficult to access, fuse and mine due to disparate nature and granularity. If the data from these independent systems are combined into a common correlated data source, this new set of information could add value to the individual data sources by the means of data mining. This paper proposes a knowledge discovery process based on CRISP-DM for failure diagnosis using big data sets. The process is exemplified by applying it on railway infrastructure assets. The proposed framework implies a progress beyond the state of the art in the development of Big Data technologies in the fields of Knowledge Discovery algorithms from heterogeneous data sources, scalable data structures, real-time communications and visualizations techniques.

  • 45.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Advanced Analytics for Modern Mining2022Ingår i: Advanced Analytics in Mining Engineering: Leverage Advanced Analytics in Mining Industry to Make Better Business Decisions / [ed] Soofastaei, A., Springer Nature, 2022, s. 23-54Kapitel i bok, del av antologi (Övrigt vetenskapligt)
  • 46.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Robotics and artificial intelligence (AI) for maintenance2023Ingår i: Monitoring and Protection of Critical Infrastructure by Unmanned Systems / [ed] Pasquale Daponte, Florentin Paladi, IOS Press , 2023, s. 206-223Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    This paper reviews the application of AI in maintenance and inspections. It gives an overview of the development of AVs and distant inspection operations for industrial assets using unmanned aerial vehicles (UAVs). It discusses the use of AVs in infrastructure inspection and explain the types of sensors used for these applications. It explains how autonomous robots, including drones, are currently used in various industrial settings for inspection and maintenance. The paper concludes by discussing the use of AI in predictive maintenance.

  • 47.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    SMART bearing: from sensing to actuation2013Ingår i: Proceedings of the 12th IMEKO TC10 Workshop on Technical Diagnostics: New Perspectives in Measurements, Tools and Techniques for Industrial Applications, Florence, Italy: IMEKO , 2013, s. 21-30Konferensbidrag (Refereegranskat)
    Abstract [en]

    A Centre has been created within Lulea University of Technology (LTU), with support from SKF, to examine the opportunities presented by developments in sensing and communication technologies in adding sensing and analysis functionality to rolling element bearings. One of the goals is the development of an artefact which closes the loop from sensing to actuation and taking advantage of all existing IT, mechanical and maintenance technologies.

  • 48.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Berges, Luis
    Department Design engineering and manufacturing, University of Zaragoza.
    Sandborn, Peter
    CAlCe Center for Advanced life Cycle engineering, University of Maryland.
    The need for aggregated indicators in performance asset management2014Ingår i: Eksploatacja i Niezawodność – Maintenance and Reliability, ISSN 1507-2711, E-ISSN 2956-3860, Vol. 16, nr 1, s. 120-127Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Composite indicators formed when individual Indicators are compiled into a single index. A composite indicator should ideally measure multidimensional concepts that cannot be captured by a single index. Since asset management is multidisciplinary,composite indicators would be helpful. This paper describes a method of monitoring a complex entity in a processing plant. In this scenario, a composite use index from a combination of lower level use indices and weighting values. Each use index contains status information on one aspect of the lower level entities, and each weighting value corresponds to one lower level entity. The resulting composite indicator can be a decision-making tool for asset managers.

  • 49.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Fuqing, Yuan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    RUL prediction using moving trajectories between SVM hyper planes2012Ingår i: 2012 proceedings: Annual Reliability and Maintainability Symposium (RAMS 2011) : Reno, Nv 23-26 Jan. 2012, Piscataway, NJ: IEEE Communications Society, 2012Konferensbidrag (Refereegranskat)
    Abstract [en]

    With increasing amounts of data being generated by businesses and researchers, there is a need for fast, accurate and robust algorithms for data analysis. Improvements in database's technology, computing performance and artificial intelligence have contributed to the development of intelligent data analysis. The primary aim of data mining is knowledge discovery, i.e. patterns in the data that lead to better understanding of the data generating process and to useful predictions. The knowledge that becomes available through data mining enables an asset owner to make important decisions about life cycle costs in advance. In maintenance field, CMMS (Computer maintenance management system) and CM (Condition Monitoring) are the most popular software available in the industries. Since first one stores all historical data, maintenance actions, events and ma nufacturer recommendations, second one collects and stores all critical physical parameters (vibration, temperature.) to be monitored in a regular time basis. However, converting these data into useful information is a challenge. The degradation process of a system may be affected by many unknown factors, such as unidentified fault modes, unmeasured operational conditions, engineering variance, environmental conditions, etc. These unknown factors not only complicate the degradation behaviors of the system, but also make it difficult to collect quality data. Due to lack of knowledge and incomplete measurements, certain important con text information (e.g. fault modes, operational conditions) of the collected data will be missing. Therefore, historical data of the system with a large variety of degradation patterns will be mixed together. With such data, learning a global model for Remaining Useful Life (RUL) prediction becomes extremely hard since the end user does not have enough and good-quality data to model properly the system. This has led us to look for advanced RUL prediction techniques beyond the traditional RUL prediction models. The degradation process for many engineering systems, especially mechanical systems, is irreversible unless the condition is recovered by effective maintenance actions. The irreversible degradation process does not necessarily imply that the observed features will exhibit a monotonic progression pattern during degradation. Such progression pattern is sometimes hard to model using parametric methods. Considering a degradation process involving no or limited maintenance, the process may compose of a sequence of irreversible stages (either discrete or continuous) from new to be worn out, which can be implicitly expressed by the trajectory of the measured condition data or features. Therefore, the RUL of the system can be estimated if its future degradation trend can be projected from those historical instances. In this paper, a novel RUL prediction method inspired by feature maps and SVM classifiers is proposed. The historical instances of a system with life-time condition data are used to create a classification by SVM hyper planes. For a test instance of the same system, whose RUL is going to be estimated, degradation speed is evaluated by computing the minimal distance defined based on the degradation trajectories, i.e. the approach of the system to the hyper plane that segregates good and bad condition data at a different time horizon. Therefore, the final RUL of a specific component can be estimated, and global RUL information can then be obtained by aggregating the multiple RUL estimations using a density estimation method. Proposed model develops an effective RUL prediction method that addresses multiple challenges in complex system prognostics, where many parameters are unknown. Similarities between degradation trajectories can be checked in order to enrich existing methodologies in prognostic's applications. Existing CM data for bearings will be used to verify the model.

  • 50.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Juuso, Esko
    University of Oulu.
    Lahdelma, Sulo
    University of Oulu.
    Fusion of maintenance and control data: a need for the process2012Ingår i: Proceedings of 18th World Conference on Nondestructive Testing: April 2012, Durban, South Africa, 2012Konferensbidrag (Refereegranskat)
    Abstract [en]

    A process control system deals with disperse information sources mostly related with operation and maintenance issues. 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 a process plant from various sources or functional areas of the plant inc1uding, for example, the process control functional areas, the maintenance functional areas and the process performance monitoring functional areas. This data and information is manipulated in a coordinated manner by the cloud using XML for data exchange, and is redistributed to other applications where is used to perform overall better or more optimal control, maintenance and business activities. From maintenance point of view, the benefit is that information or data may be collected by maintenance functions pertaining to the health, variability, performance or utilization of an asset. The end user, i.e. operators and maintainers are also considered. A user interface becomes necessary in order to enable users to access and manipulate the data and optimize plant operation. Furthermore, applications, such as work order generation applications may automatically generate work orders, parts or supplies orders, etc. based on events occurring within the plant due to this integration of data and creation of new knowledge as a consequence of such process.

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