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  • 1.
    Teymourian, Kiumars
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Seneviratne, Dammika
    Tecnalia Research and Innovation, La Almunia, Zaragoza, Spain.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Tecnalia Research and Innovation, La Almunia, Zaragoza, Spain.
    Integrating Ergonomics in Maintanability: A Case Study from Manufacturing Industry2019Ingår i: Journal of Industrial Engineering and Management Science, E-ISSN 2446-1822, Vol. 2018, nr 1, s. 131-150, artikel-id 8Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Maintainability is key part of Reliability, Availability, Maintainability and Safety (RAMS) estimation and prediction in complex assets. Indeed, availability calculation comprises accurate estimation of maintainability and frequently, it is just a time stamp for mean time to repair (MTTR) estimations. However, maintainability is a human related figure where the skill, capabilities, tools and the design of the asset play key role in its performance. The aim of this article is to describe the effects of ergonomists’ contribution during maintainability process for system/products design. System designer thinking in system and its subsystem in a way of technical functionality. On the other hand, ergonomists are expertise in human capability and limitation. If human become a part of system than their interface and interaction become crucial factors in a success of system performance and its sustainability. In this paper, it has discussed three main issues that help the process of maintainability design. These issues are safety, task analysis and risk analysis. It has also touched reliability engineer’s task to increase Overall Equipment Effectiveness (OEE). These issues are explained via a case study from a manufacturing industry.

  • 2.
    Zhang, Shuangsheng
    et al.
    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China.Xuzhou Urban Water Resources Management Office, Xuzhou, China.
    Liu, Hanhu
    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China..
    Qiang, Jing
    School of Mathematics, China University of Mining and Technology, Xuzhou, China.
    Gao, Hongze
    GHD Services, Inc, Waterloo, Ontario, Canada.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Optimization of Well Position and Sampling Frequency for Groundwater Monitoring and Inverse Identification of Contamination Source Conditions Using Bayes’ Theorem2019Ingår i: CMES - Computer Modeling in Engineering & Sciences, ISSN 1526-1492, E-ISSN 1526-1506, Vol. 119, nr 2, s. 373-394Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Coupling Bayes’ Theorem with a two-dimensional (2D) groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity (M ), release location ( X0 , Y0) and release time (T0), based on monitoring well data. To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters, a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy. To demonstrate how the model works, an exemplar problem with an instantaneous release of a contaminant in a confined groundwater aquifer was employed. The information entropy of the model parameters posterior distribution was used as a criterion to evaluate the monitoring data quantity index. The optimal monitoring well position and monitoring frequency were solved by the two-step Monte Carlo method and differential evolution algorithm given a known well monitoring locations and monitoring events. Based on the optimized monitoring well position and sampling frequency, the contamination source was identified by an improved Metropolis algorithm using the Latin hypercube sampling approach. The case study results show that the following parameters were obtained: 1) the optimal monitoring well position (D) is at (445, 200); and 2) the optimal monitoring frequency (Δt) is 7, providing that the monitoring events is set as 5 times. Employing the optimized monitoring well position and frequency, the mean errors of inverse modeling results in source parameters (M, X0 ,Y0 ,T0 ) were 9.20%, 0.25%, 0.0061%, and 0.33%, respectively. The optimized monitoring well position and sampling frequency can effectively safeguard the inverse modeling results in identifying the contamination source parameters. It was also learnt that the improved Metropolis-Hastings algorithm (a Markov chain Monte Carlo method) can make the inverse modeling result independent of the initial sampling points and achieves an overall optimization, which significantly improved the accuracy and numerical stability of the inverse modeling results.

  • 3.
    Raposo, Hugo
    et al.
    Coimbra University-UC, Department of Mechanical Engineering, Centre for Mechanical Engineering, Materials and Processes (CEMMPRE), Coimbra, Portugal.
    Torres Farinha, José
    Coimbra University-UC, Department of Mechanical Engineering, Centre for Mechanical Engineering, Materials and Processes (CEMMPRE), Coimbra, Portugal.
    Fonseca, Inácio
    Coimbra University-UC, Department of Mechanical Engineering, Centre for Mechanical Engineering, Materials and Processes (CEMMPRE), Coimbra, Portugal.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Predicting condition based on oil analysis: A case study2019Ingår i: Tribology International, ISSN 0301-679X, E-ISSN 1879-2464, Vol. 135, s. 65-74Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The paper presents and discusses a model for condition monitoring. Using data from the oil in the Diesel engines of a fleet of urban buses, it studies the evolution of degradation and develops a predictive maintenance policy for oil replacement. Based on the analysis of the oil condition, the intervals of oil replacement can be expanded, allowing increased availability. The paper links time series forecasting with the statistical behavior of some oil effluents, like soot. This exercise can be expanded to include other variables, and the model has the potential to be applied to other physical assets to achieve the best availability based on a condition monitoring policy.

  • 4.
    González-González, Asier
    et al.
    Tecnalia Research and Innovation, Industry and Transport Division, Miñano.
    Jimenez Cortadi, Alberto
    Tecnalia Research and Innovation, Industry and Transport Division, Miñano.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Tecnalia Research and Innovation, Industry and Transport Division, Miñano .
    Ciani, Lorenzo
    University of Florence, Department of Information Engineering.
    Condition Monitoring of Wind Turbine Pitch Controller: A Maintenance Approach2018Ingår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 123, s. 80-93Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    With the increase of wind power capacity worldwide, researchers are focusing their attention on the operation and maintenance of wind turbines. A proper pitch controller must be designed to extend the life cycle of a wind turbine’s blades and tower. The pitch control system has two main, but conflicting, objectives: to maximize the wind energy captured and converted into electrical energy and to minimize fatigue and mechanical load. Four metrics have been proposed to balance these two objectives. Also, diverse pitch controller strategies are proposed in this paper to evaluate these objectives. This paper proposes a novel metrics approach to achieve the conflicting objectives with a maintenance focus. It uses a 100 kW wind turbine as a case study to simulate the proposed pitch control strategies and evaluate with the metrics proposed. The results are showed in two tables due to two different wind models are used.

  • 5.
    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.

  • 6.
    Raposo, Hugo
    et al.
    CEMMPRE, Coimbra, Portugal.
    Torres Farinha, José
    CEMMPRE, Coimbra, Portugal.
    Ferreira, Luís
    FEUP, Porto, Portugal.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Dimensioning reserve bus fleet using life cycle cost models and condition based/predictive maintenance: a case study2018Ingår i: Public Transport, ISSN 1866-749X, E-ISSN 1613-7159, Vol. 10, nr 1, s. 169-190Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The paper demonstrates the dependence of a fleet reserve of buses on the maintenance policy of the whole fleet, in particular, condition-based maintenance using a motor oil degradation analysis. The paper discusses an approach to evaluate the oil degradation and the prediction of the next value for one relevant oil variable. The methodology to evaluate the reserve fleet is based on bus availability, estimated through the mean time between failures and the mean time to repair ratios. Through the use of econometric models, it is possible to determine the most rational size of the reserve fleet.

  • 7.
    Teymourian, Kiumars
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Seneviratne, Dammika
    Technalia Research and Innovation, La Amunia, Zaragoza, Spain.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Technalia Research and Innovation, La Amunia, Zaragoza, Spain.
    Ergonomics in Maintainability: System and Product Design Process2018Ingår i: Proceedings of Maintenance Preformance Measurement and Magangement (MPMM), 2018Konferensbidrag (Refereegranskat)
  • 8.
    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.

  • 9.
    Kumar, Uday
    et al.
    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.
    Maintenance in the Era of Industry 4.0: Issues and Challenges2018Ingår i: Quality, IT and Business Operations: Modeling and Optimization / [ed] Kapur P., Kumar U., Verma A., Singapore: Springer, 2018, s. 231-250Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

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

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

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

  • 10.
    Seneviratne, D.
    et al.
    Tecnalia Research and Innovation, Industry and Transport Division, Miñano (Araba), Spain.
    Ciani, L.
    Department of Information Engineering, University of Florence, Florence, Italy.
    Catelani, M.
    Department of Information Engineering, University of Florence, Florence, Italy.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Tecnalia Research and Innovation, Industry and Transport Division, Miñano (Araba), Spain.
    Smart maintenance and inspection of linear assets: An Industry 4.0 approach2018Ingår i: Acta IMEKO, ISSN 0237-028X, Vol. 7, nr 1, s. 50-56Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Linear assets have linear properties, for instance, similar underlying geometry and characteristics, over a distance. They show specific patterns of continuous inherent deteriorations and failures. Therefore, remedial inspection and maintenance actions will be similar along the length of a linear asset, but because as the asset is distributed over a large area, the execution costs are greater. Autonomous robots, for instance, unmanned aerial vehicles, pipe inspection gauges, and remotely operated vehicles, are used in different industrial settings in an ad-hoc manner for inspection and maintenance. Autonomous robots can be programmed for repetitive and specific tasks; this is useful for the inspection and maintenance of linear assets. This paper reviews the challenges of maintaining the linear assets, focusing on inspections. It also provides a conceptual framework for the use of autonomous inspection and maintenance practices for linear assets to reduce maintenance costs, human involvement, etc., whilst improving the availability of linear assets by effective use of autonomous robots and data from different sources.

  • 11.
    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.

  • 12.
    Simon, Victor
    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.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Aggregation of Electric Current Consumption Features to Extract Maintenance KPIs2017Ingår i: Management Systems in Production Engineering, ISSN 2299-0461, Vol. 25, nr 3, s. 183-190Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    All electric powered machines offer the possibility of extracting information and calculating Key Performance Indicators (KPIs) from the electric current signal. Depending on the time window, sampling frequency and type of analysis, different indicators from the micro to macro level can be calculated for such aspects as maintenance, production, energy consumption etc. On the micro-level, the indicators are generally used for condition monitoring and diagnostics and are normally based on a short time window and a high sampling frequency. The macro indicators are normally based on a longer time window with a slower sampling frequency and are used as indicators for overall performance, cost or consumption. The indicators can be calculated directly from the current signal but can also be based on a combination of information from the current signal and operational data like rpm, position etc. One or several of those indicators can be used for prediction and prognostics of a machine's future behavior. This paper uses this technique to calculate indicators for maintenance and energy optimization in electric powered machines and fleets of machines, especially machine tools.

  • 13.
    Raposo, Hugo
    et al.
    CEMMPRE - Centre for Mechanical Engineering, Materials and Processes, University of Coimbra.
    Farinha, José Torres
    CEMMPRE - Centre for Mechanical Engineering, Materials and Processes, University of Coimbra.
    Ferreira, Luis A.
    UP – University of Porto.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    An integrated econometric model for bus replacement and determination of reserve fleet size based on predictive maintenance2017Ingår i: Eksploatacja i Niezawodnosc, ISSN 1507-2711, Vol. 19, nr 3, s. 358-368Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Maintenance policies influence equipment availability and, thus, they affect a company’s capacity for productivity and competitiveness. It is important to optimize the Life Cycle Cost (LCC) of assets, in this case, passenger bus fleets. The paper presents a predictive condition monitoring maintenance approach based on engine oil analysis, to assess the potential impact of this variable on the availability of buses. The approach has implications on maintenance costs during the life of a bus and, consequently, on the determination of the best time for bus replacement. The paper provides an overview of economic replacement models through a global model, with an emphasis on availability and its dependence on maintenance and maintenance costs. These factors help to determine the size of the reserve fleet and guarantee availability

  • 14.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan Technology Research Centre, Control and Monitoring Area.
    Salgado, Oscar
    IK4-Ikerlan Technology Research Centre, Control and Monitoring Area.
    Ciani, Lorenzo
    University of Florence, Department of Information Engineering.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Catelani, Marcantonio
    University of Florence, Department of Information Engineering.
    Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data2017Ingår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 108, s. 152-162Artikel i tidskrift (Refereegranskat)
    Abstract [en]

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

  • 15.
    Seneviratne, Dammika
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Boyang, Shi
    Tecnalia Research and Innovation, Industry and Transport Division, San-Sebastian.
    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.
    Autonomous inspection and maintenance of linear assets2017Ingår i: 15th IMEKO TC10 Workshop on Technical Diagnostics 2017: "Technical Diagnostics in Cyber-Physical Era", 2017, s. 194-199Konferensbidrag (Refereegranskat)
    Abstract [en]

    Linear assets have linear properties, for instance, similar underlying geometry and characteristics over a distance. They show specific patterns of continuous inherent deteriorations and failures. Therefore, remedial inspection and maintenance actions will be similar along the length of a linear asset, but because it is distributed over a large area, the execution costs are greater. Autonomous robots can be programmed for repetitive and specific tasks. Unmanned aerial vehicles and remotely operated vehicles are currently used in different industrial settings in ad-hoc manner for inspection and maintenance purposes. This manuscript provides a conceptual framework for the use of autonomous inspection and maintenance practices for linear assets to reduce maintenance costs, human involvement, etc., whilst improving the availability of linear assets by effective utilization of autonomous robots and data from different sources

  • 16.
    Villarejo, Roberto
    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.
    Urko, Leturiondo
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4 Ikerlan, P J Ma Arizmendiarrieta 2, Arrasate Mondragon 20500, Spain.
    Simon, Victor
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Seneviratne, Dammika
    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.
    Bottom to Top Approach for Railway KPI Generation2017Ingår i: Management Systems in Production Engineering, ISSN 2299-0461, Vol. 25, nr 3, s. 191-198, artikel-id 28Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Railway maintenance especially on infrastructure produces a vast amount of data. However, having data is not synonymous with having information; rather, data must be processed to extract information. In railway maintenance, the development of key performance indicators (KPIs) linked to punctuality or capacity can help planned and scheduled maintenance, thus aligning the maintenance department with corporate objectives. There is a need for an improved method to analyse railway data to find the relevant KPIs. The system should support maintainers, answering such questions as what maintenance should be done, where and when. The system should equip the user with the knowledge of the infrastructure's condition and configuration, and the traffic situation so maintenance resources can be targeted to only those areas needing work. The amount of information is vast, so it must be hierarchized and aggregated; users must filter out the useless indicators. Data are fused by compiling several individual indicators into a single index; the resulting composite indicators measure multidimensional concepts which cannot be captured by a single index. The paper describes a method of monitoring a complex entity. In this scenario, a plurality of use indices and weighting values are used to create a composite and aggregated use index from a combination of lower level use indices and weighting values. The resulting composite and aggregated indicators can be a decision-making tool for asset managers at different hierarchical levels.

  • 17.
    González-González, Asier
    et al.
    Tecnalia Research and Innovation, Industry and Transport Division, Miñano.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Condition monitoring of wind turbine pitch controller: A maintenance approach2017Ingår i: 15th IMEKO TC10 Workshop on Technical Diagnostics 2017: "Technical Diagnostics in Cyber-Physical Era", 2017, s. 200-206Konferensbidrag (Refereegranskat)
    Abstract [en]

    Due to the wind power capacity energy grow exponential, interest in operation maintenance is increasing. A proper pitch controller must be designed to extend the life cycle of some wind turbine (WT) components such as blades or tower. The pitch control system has two main, but conflicting, objectives. On the one hand, it seeks to maximize the wind energy captured and converted into electrical energy. On the other hand, it seeks to minimize fatigue and mechanical load. Various metrics are proposed to achieve a compromise solution that balances these objectives. A WT of 100 kW is used to validate pitch control strategies

  • 18.
    Schmidt, Bernard
    et al.
    Högskolan i Skövde.
    Gandhi, Kanika
    Högskolan i Skövde.
    Wang, Lihui
    bSchool of Engineering Science, Kungliga Tekniska Högskolan, Stockholm.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Context preparation for predictive analytics: a case from manufacturing industry2017Ingår i: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, nr 3, s. 341-354Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Purpose

    The purpose of this paper is to exemplify and discuss the context aspect for predictive analytics where in parallel condition monitoring measurements data and information related to the context are gathered and analysed.

    Design/methodology/approach

    This paper is based on an industrial case study, conducted in a manufacturing company. The linear axis of a machine tool has been selected as an object of interest. Available data from different sources have been gathered and a new condition monitoring function has been implemented. Details about performed steps of data acquisition and selection are provided. Among the obtained data, health indicators and context related information have been identified.

    Findings

    Multiple sources of relevant contextual information has been identified. Performed analysis discovered the deviations in operational conditions when the same machining operation is repeatedly performed.

    Originality/value

    This paper shows the outcomes from a case study in real word industrial setup. A new visualisation method of gathered data is proposed to support decision-making process

  • 19.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Scholz, Dieter
    Hamburg University of Applied Sciences, Aero - Aircraft Design and Systems Group.
    Decision trees and the effects of feature extraction parameters for robust sensor network design2017Ingår i: Eksploatacja i Niezawodnosc - Maintenance and Reliability, ISSN 1507-2711, Vol. 19, nr 1, s. 31-42Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Reliable sensors and information are required for reliable condition monitoring. Complex systems are commonly monitored by many sensors for health assessment and operation purposes. When one of the sensors fails, the current state of the system cannot be calculated in same reliable way or the information about the current state will not be complete. Condition monitoring can still be used with an incomplete state, but the results may not represent the true condition of the system. This is especially true if the failed sensor monitors an important system parameter. There are two possibilities to handle sensor failure. One is to make the monitoring more complex by enabling it to work better with incomplete data; the other is to introduce hard or software redundancy. Sensor reliability is a critical part of a system. Not all sensors can be made redundant because of space, cost or environmental constraints. Sensors delivering significant information about the system state need to be redundant, but an error of less important sensors is acceptable. This paper shows how to calculate the significance of the information that a sensor gives about a system by using signal processing and decision trees. It also shows how signal processing parameters influence the classification rate of a decision tree and, thus, the information. Decision trees are used to calculate and order the features based on the information gain of each feature. During the method validation, they are used for failure classification to show the influence of different features on the classification performance. The paper concludes by analysing the results of experiments showing how the method can classify different errors with a 75% probability and how different feature extraction options influence the information gain

  • 20.
    Teymourian, Kiumars
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Seneviratne, Dammika
    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.
    Ergonomics contribution in maintainability2017Ingår i: Management Systems in Production Engineering, ISSN 2299-0461, Vol. 25, nr 3, s. 217-223, artikel-id 31Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The objective of this paper is to describe an ergonomics contribution in maintainability. The economical designs, inputs and training helps to increase the maintainability indicators for industrial devices. This analysis can be helpful, among other cases, to compare systems, to achieve a better design regarding maintainability requirements, to improve this maintainability under specific industrial environment and to foresee maintainability problems due to eventual changes in a device operation conditions. With this purpose, this work first introduces the notion of ergonomics and human factors, maintainability and the implementation of assessment of human postures, including some important postures to perform maintenance activities. A simulation approach is used to identify the critical posture of the maintenance personnel and implements the defined postures with minimal loads on the personnel who use the equipment in a practical scenario. The simulation inputs are given to the designers to improve the workplace/equipment in order to high level of maintainability. Finally, the work concludes summarizing the more significant aspects and suggesting future research.

  • 21.
    Teymourian, Kiumars
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Seneviratne, Dammika
    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.
    Ergonomics Contribution in Maintainability2017Ingår 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. 180-186Konferensbidrag (Refereegranskat)
    Abstract [en]

    The objective of this paper is to describe an ergonomics contribution in maintainability. The economical designs, inputs and training helps to increase themaintainability indicators for industrial devices. This analysis can be helpful, among other cases, to compare systems, to achieve a better design regarding maintainability requirements, to improve this maintainability under specific industrial environment and to foresee maintainability problems due to eventual changes in a device operation condition. With this purpose, this work first introduces the notion of ergonomics and human factors, maintainability and the implementation of assessment of human postures, including some important postures to perform maintenance activities. A simulation approach is used to identify the critical posture of the maintenance personnel and implements the defined postures with minimal loads on the personnel who use the equipment in a practical scenario. The simulation inputs are given to the designers to improve the workplace/equipment in order to high level of maintainability. Finally, the work concludes summarizing the more significant aspects and suggesting future research.

  • 22.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Scholz, Dieter
    Hamburg University of Applied Sciences, Aero - Aircraft Design and Systems Group.
    Genetic algorithms and decision trees for condition monitoring and prognosis of A320 aircraft air conditioning2017Ingår i: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 59, nr 8, s. 424-433Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Unscheduled maintenance is a large cost driver for airlines, but condition monitoring and prognosis can reduce the number of unscheduled maintenance actions. This paper discusses how condition monitoring can be introduced into most systems by adopting a data-driven approach and using existing data sources. The goal is to forecast the remaining useful life (RUL) of a system based on various sensor inputs. Decision trees are used to learn the characteristics of a system. The data for the decision tree training and classification are processed by a generic parametric signal analysis. To obtain the best classification results for the decision tree, the parameters are optimised by a genetic algorithm. A forest of three different decision trees with different signal analysis parameters is used as a classifier. The proposed method is validated with data from an A320 aircraft from Etihad Airways. Validation shows that condition monitoring can classify the sample data into ten predetermined categories, representing the total useful life (TUL) in 10% steps. This is used to predict the RUL. There are 350 false classifications out of 850 samples. Noise reduction reduces the outliers to nearly zero, making it possible to correctly predict condition. It is also possible to use the classification output to detect a maintenance action in the validation data.

  • 23.
    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.

  • 24.
    Santelices, Gabriel
    et al.
    Department of Mining Engineering, Pontificia Universidad Católica de Chile, Santiago.
    Pascual, Rodrigo
    Pontificia Universidad Católica de Chile, Department of Mining Engineering, Pontificia Universidad Católica de Chile, Santiago.
    Lüer-Villagra, Armin
    Department of Engineering Sciences, Universidad Andres Bello, Santiago.
    Cawley, Alejandro Mac
    Department of Industrial Engineering, Pontificia Universidad Católica de Chile, Santiago.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Integrating mining loading and hauling equipment selection and replacement decisions using stochastic linear programming2017Ingår i: International Journal of Mining, Reclamation and Environment, ISSN 1748-0930, E-ISSN 1748-0949, Vol. 31, nr 1, s. 52-65Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Equipment selection is a key strategic decision in the design of a material handling system, because an improper one will lead to operational problems and unnecessary investment costs. It involves determining the number and combination of loaders and trucks which will move the material, fulfilling a specified production schedule. Previous works have addressed this problem with deterministic approaches, without considering the inter-dependent availability of trucks and loaders. In order to fill this gap, we developed a stochastic model that combines the selection and equipment replacement problems, subject to a stochastic production rate constraint. This is a new idea that will help decision-makers to decide faster and more reliable. The proposed model optimises the fleet by minimising the total life cycle costs. To solve it, we used a linearisation approach that reduces the computational effort. We tested our approach with a benchmark model, using a mining case study. Results indicate that the solutions ensure with a high probability a determined production target, producing good robust solutions compared to the deterministic counterpart.

  • 25.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Seneviratne, DammikaLuleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Management Systems in Production Engineering: Maintenance Performance Measurement and Management Challenges:  From Sensing to Decision Support2017Samlingsverk (redaktörskap) (Övrigt vetenskapligt)
  • 26.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan Technology Research centre, Control and Monitoring Area, Arrasate-Mondragon, Spain.
    Salgado, Oscar
    IK4-Ikerlan Technology Research centre, Control and Monitoring Area, Arrasate-Mondragon, Spain.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Methodology for the physics-based modelling of multiple rolling element bearing configurations2017Ingår i: Proceedings of the Institution of mechanical engineers. Proceedings part K, journal of multi-body dynamics, ISSN 1464-4193, E-ISSN 2041-3068, Vol. 231, nr 1, s. 194-212Artikel i tidskrift (Refereegranskat)
    Abstract [en]

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

  • 27.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Seneviratne, DammikaLuleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    MPMM 2016, Maintenance, Performance, Measurement & Management: conference proceedings2017Proceedings (redaktörskap) (Refereegranskat)
    Abstract [en]

    The maintenance function is inherent to production but its activities are not always understood or quantified. A characteristic of maintenance is that its activity involves more than a group of people or a workshop and goes beyond the limits of a traditional department.

    The scope of maintenance in a manufacturing environment is illustrated by its various definitions. British Standards Institute defines maintenance as a combination of all technical and associated administrative activities required to keep equipment, installations and other physical assets in the desired operating condition or restore them to this condition, some authors indicate that maintenance is about achieving the required asset capabilities within an economic or business context, or consists of the engineering decisions and associated actions necessary and sufficient for the optimization of specified equipment ‘capability’ where capability is the ability to perform a specified function within a range of performance levels that may relate to capacity, rate, quality, safety and responsiveness. However, they all agree that the objective of maintenance is to achieve the agreed-upon output level and operating pattern at minimum resource cost within the constraints of system condition and safety.

    We can summarize the maintenance objectives under the following categories: ensuring asset functions (availability, reliability, product quality etc.); ensuring design life; ensuring asset and environmental safety; ensuring cost effectiveness in maintenance; ensuring efficient use of resources (energy and raw materials). For production equipment, ensuring the system functions as it should is the prime maintenance objective. Maintenance must provide the required reliability, availability, efficiency and capability of production systems. Ensuring system life refers to keeping the equipment in good condition to achieve or prolong its designed life. In this case, cost has to be optimized to achieve the desired plant condition. Asset safety is very important, as failures can have catastrophic consequences. The cost of maintenance has to be minimized while keeping the risks within strict limits and meeting the statutory requirements.

    For a long time, maintenance was carried out by the workers themselves, in a more loosely organized style of maintenance with no haste for the machinery or tools to be operational again. However, things have changed.

    •        First, there is a need for higher asset availability. With scale economies dominating the global map, the demand for products is increasing. However, companies suffer financially from the costs of expansion, purchase of industrial buildings, production equipment, acquisitions of companies in the same sector, and so on. Productive capacities must be kept at a maximum, and organizations are beginning to worry about keeping track of the parameters that may affect the availability of their plants and machinery.

    •        The second concern follows from the first. When organizations begin to optimize their production costs and create cost models attributable to the finished product, they start to question maintenance cost. This function has grown to include assets, personnel etc., consuming a significant percentage of the overall organization budget. Therefore, when companies are establishing policies to streamline costs, the question of the maintenance budget arises, followed by questions about the success of this budget. They start to consider availability and quality parameters.

    A question that has haunted maintenance throughout history now appears: how do we maximize availability at the lowest cost? To answer this question, various methodologies, technologies and batteries of indicators are being developed to observe the impacts of improvements.

  • 28.
    Lopes, Julio C.O.
    et al.
    Division of Mechanical Engineering, Technological Institute of Aeronautics, São José dos Campo.
    Scarpel, Rodrigo
    Division of Mechanical Engineering, Technological Institute of Aeronautics, São José dos Campo.
    Abrahão, Fernando T.M.
    Laboratory of Logistics Engineering, Technological Institute of Aeronautics, São José dos Campo.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ciani, Lorenzo
    University of Florence, Department of Information Engineering.
    Optimization in performance-based logistics contracts2017Ingår i: 4th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2017: proceedings, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, s. 413-418, artikel-id 7999608Konferensbidrag (Refereegranskat)
    Abstract [en]

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

  • 29.
    Lopes, Julio C.O.
    et al.
    Division of Mechanical Engineering, Technological Institute of Aeronautics, São José dos Campo.
    Scarpel, Rodrigo
    Division of Mechanical Engineering, Technological Institute of Aeronautics, São José dos Campo.
    Abrahão, Fernando T.M.
    Laboratory of Logistics Engineering, Technological Institute of Aeronautics, São José dos Campo.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ahmadi, Alireza
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Optmization In Performance-Based Logistics Contracts2017Ingår 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. 133-138Konferensbidrag (Refereegranskat)
    Abstract [en]

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

  • 30.
    Ghodrati, Behzad
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ahmadi, Alireza
    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.
    Reliability Analysis of Switches and Crossings: A Case Study in Swedish Railway2017Ingår i: International Journal of Railway Research, ISSN 2361-5376, Vol. 4, nr 1, s. 1-12Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    It is reported that switches and crossings (S&C) are one of the subsystems that cause the most delays on Swedish Railways while accounting for at least 13% of maintenance costs [6]. It is the main reason why we chose to base our study on this subsystem.

    Intelligent data processing allows understanding the real reliability characteristics of the assets to be maintained. The first objective of this research is to determine the S&C reliability characteristics based on field data collection. Because field failure data are typically strongly censored, an especial statistics software package was developed to process field failure data, as commercial packages have not been found satisfactory in that respect. The resulting software, named RDAT® (Reliability Data Analysis Tool) has been relied upon for this study: it is especially adapted to statistical failure data analysis.

    In the next step the availability of studied switches and crossings is estimated based on the reliability characteristics founded in the first step.

  • 31.
    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.

  • 32.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan Technology Research Centre.
    Salgado, Oscar
    IK4-Ikerlan Technology Research Centre.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Validation of a physics-based model of a rotating machine for synthetic data generation in hybrid diagnosis2017Ingår i: Structural Health Monitoring, ISSN 1475-9217, E-ISSN 1741-3168, Vol. 16, nr 4, s. 458-470Artikel i tidskrift (Refereegranskat)
    Abstract [en]

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

  • 33.
    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

  • 34.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Scholz, Dieter
    Hamburg University of Applied Sciences, Aero - Aircraft Design and Systems Group.
    Automated parameter optimization for feature extraction for condition monitoring2016Ingår i: 14th IMEKO TC10 Workshop on Technical Diagnostics 2016: New Perspectives in Measurements, Tools and Techniques for Systems Reliability, Maintainability and Safety, Milan, Italy, 27-28 June 2016, 2016, s. 452-457Konferensbidrag (Refereegranskat)
    Abstract [en]

    Pattern recognition and signal analysis can be used to support and simplify the monitoring of complex aircraft systems. For this purpose, information must be extracted from the gathered data in a proper way. The parameters of the signal analysis need to be chosen specifically for the monitored system to get the best pattern recognition accuracy. An optimization process to find a good parameter set for the signal analysis has been developed by the means of global heuristic search and optimization. The computed parameters deliver slightly (one to three percent) better results than the ones found by hand. In addition it is shown that not a full set of data samples is needed. It is also concluded that genetic optimization shows the best performance

  • 35.
    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.

  • 36.
    Schmidt, Bernard
    et al.
    University of Skövde.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Wang, Lihui
    University of Skövde.
    Context Awareness in Predictive Maintenance2016Ingår 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. 197-211Konferensbidrag (Refereegranskat)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance approach utilizes the condition monitoring (CM) data to predict the future machine conditions and makes decisions upon this prediction. Recent development in CM leads to context aware approach where in parallel with CM measurements also data and information related to the context are gathered. Context could be operational condition, history of machine usage and performed maintenance actions. In general more obtained information gives better accuracy of prediction. It is important to track operational context in dynamically changing environment. Today in manufacturing we can observe shift from mass production to mass customisation. This leads to changes from long series of identical products to short series of different variants. Therefore implies changing operational conditions for manufacturing equipment. Moreover, where asset consist of multiple identical or similar equipment the context aware method can be used to combine in reliable way information. This should allow to increase accuracy of prediction for population as a whole as well as for each equipment instances. Same of those data have been already recorded and stored in industrial IT systems. However, it is distributed over different IT systems that are used by different functional units (e.g. maintenance department, production department, quality department, tooling department etc.). This paper is a conceptual paper based on initial research work and investigation in two manufacturing companies from automotive industry.

  • 37.
    Thaduri, Adithya
    et al.
    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.
    Verma, Ajit Kumar
    Department of Electrical Engineering, Indian Institute of Technology, Bombay, International Institute of Information Technology, P-14, Pune Infotech Park, Phase-1, Hinjawadi, Stord/Haugesund University College, Haugesund.
    Context-Based Maintenance and Repair Shop Suggestion for a Moving Vehicle2016Ingår 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. 67-81Konferensbidrag (Refereegranskat)
    Abstract [en]

    Maintenance of moving vehicles is quite challenging because they may disrupt the normal flow of transportation due to unexpected breakdowns, slowdowns and stoppages. In order to avoid stoppages and to minimize the downtime, maintenance and condition monitoring systems must be optimized. On one hand the condition monitoring on board should provide automatic failure detection, identification and localization together with a prognostic of the future failures. On the other hand maintenance logistics and product supportability must be also optimized since the onboard system should provide a suggestion of a repair shop that depends on location, cost and availability of spare parts, technicians’ skills and queuing time for repairs. However the vehicles are independent assets interacting among them within the traffic system and also interacting with the infrastructure (roads, rails etc.) seriously affected by weather, maintenance of infra, regulations etc. Therefore the proposed solution is to equip the vehicles with a context-aware system that monitors the condition and maintenance schedules of parts and alarm the driver of the parts that are in near to repair cycle. This system will perform risk analysis and will communicate with the cloud propose a decision of selection of repair shop on the location and path of vehicle depending on weather, road and traffic, cost and availability of spare parts at respective repair shops based on risk assessment and prediction. The information contained in the cloud will also communicate the workshop that will book time slot and block the necessary spare parts for the coming vehicle minimizing waiting time. This mechanism will help in reducing unexpected stoppages, vehicle degradation and efficient spare parts management combining in a successful way the workload of the workshops from both natural sources, the time based inspections and repairs together with the reactive maintenance coming from unexpected breakdown

  • 38.
    Villarejo, Roberto
    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.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Sandborn, Peter
    University of Maryland, Department of Mechanical Engineering.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Context-driven decisions for railway maintenance2016Ingår i: Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, ISSN 0954-4097, E-ISSN 2041-3017, Vol. 230, nr 5, s. 1469-1483Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Railway assets suffer wear and tear during operation. Prognostics can be used to assess the current health of a system and predict its remaining life, based on features that capture the gradual degradation of its operational capabilities. Prognostics are critical to improve safety, plan successful work, schedule maintenance, and reduce maintenance costs and down time. Prognosis is a relatively new area; however, it has become an important part of condition-based maintenance of systems. As there are many prognostic techniques, usage must be tuned to particular applications. Broadly stated, prognostic methods are either data driven, or rule or model based. Each approach has advantages and disadvantages, depending on the hierarchical level of the analysed item; consequently, they are often combined in hybrid applications. A hybrid model can combine some or all model types; thus, more-complete information can be gathered, leading to more-accurate recognition of the impending fault state. However, the amount of information collected from disparate data sources is increasing exponentially and has different natures and granularity; therefore, there is a real need for context engines to establish meaningful data links for further exploration. This approach is especially relevant in railway systems where the maintainer and operator know some of the failure mechanisms, but the sheer complexity of the infrastructure and rolling stock precludes the development of a complete model-based approach. Hybrid models are extremely useful for accurately estimating the remaining useful life (RUL) of railway systems. This paper addresses the process of data aggregation into a contextual awareness hybrid model to obtain RUL values within logical confidence intervals so that the life cycle of railway assets can be managed and optimized.

  • 39.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Scholz, Dieter
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Effects of condition-based maintenance on costs caused by unscheduled maintenance of aircraft2016Ingår i: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 22, nr 4, s. 394-417Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    PurposeThis paper analyses the effects of condition-based maintenance based on unscheduled maintenance delays that were caused by ATA chapter 21 (air conditioning). The goal is to show the introduction of condition monitoring in aircraft systemsDesign/methodology/approachThe research was done using the Airbus In-Service database to analyse the delay causes, delay length and to check if they are easy to detect via condition monitoring or not. These results were then combined with delay costs.FindingsAnalysis shows that about 80% of the maintenance actions that cause departure delays can be prevented when additional sensors are introduced. With already existing sensors it is possible to avoid about 20% of the delay causing maintenance actions.Research limitations/implicationsThe research is limited on the data of the Airbus In-Service Database and on ATA chapter 21 (air conditioning).Practical implicationsThe research shows that delays can be prevented by using existing sensors in the air-conditioning system for condition monitoring. More delays can be prevented by installing new sensors.Originality/valueThe research focuses on the effect of the air-conditioning system of an aircraft on the delay effects and the impact of condition monitoring on delays

  • 40.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Estimation of the Reliability of Rolling Element Bearings Using a Synthetic Failure Rate2016Ingår i: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective, Springer International Publishing , 2016, 1, s. 99-112Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

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

  • 41.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Fuzzy condition monitoring of recirculation fans and filters2016Ingår i: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 7, nr 4, s. 469-479Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A reliable condition monitoring is needed to be able to predict faults. Pattern recognition technologies are often used for finding patterns in complex systems. Condition monitoring can also benefit from pattern recognition. Many pattern recognition technologies however only output the classification of the data sample but do not output any information about classes that are also very similar to the input vector. This paper presents a concept for pattern recognition that outputs similarity values for decision trees. Experiments confirmed that the method works and showed good classification results. Different fuzzy functions were evaluated to show how the method can be adapted to different problems. The concept can be used on top of any normal decision tree algorithms and is independent of the learning algorithm. The goal is to have the probabilities of a sample belonging to each class. Performed experiments showed that the concept is reliable and it also works with decision tree forests (which is shown during this paper) to increase the classification accuracy. Overall the presented concept has the same classification accuracy than a normal decision tree but it offers the user more information about how certain the classification is.

  • 42.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Salgado, Oscar
    IK4-Ikerlan.
    Hybrid modelling in condition-based maintenance for smart assets2016Konferensbidrag (Refereegranskat)
    Abstract [en]

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

  • 43.
    Linnéusson, Gary
    et al.
    School of Engineering Science, University of Skövde.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Wickelgren, Mikael
    School of Business, University of Skövde.
    In Need for Better Maintenance Cost Modelling to Support the Partnership with Manufacturing2016Ingår 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. 263-282Konferensbidrag (Refereegranskat)
    Abstract [en]

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

  • 44.
    Juuso, Esko
    et al.
    University of Oulu, Control Engineering, University of Oulu.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Intelligent Real-Time Risk Analysis for Machines and Process Devices2016Ingår 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. 229-240Konferensbidrag (Refereegranskat)
    Abstract [en]

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

  • 45.
    Papic, Ljubisa
    et al.
    University of Kragujevac.
    Kovacevic, Srdja
    University of Kragujevac.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Thaduri, Adithya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Investigation of Causes of Mining Machines Maintenance Problems2016Ingår 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. 283-299Konferensbidrag (Refereegranskat)
    Abstract [en]

    Human errors in the area of mining engineering are of critical issue that has serious concerns in safety, operation and production performance. There is a need for finding cause and effect relations with respect to the maintenance issues in order to detect, scrutinize and take necessary actions to reduce it. This paper deals with the human errors in the mining machines for the maintenance problems using fishbone cause and effect analysis. The investigation of these causes and effects are carried out during different operating conditions in typical mining industry and potential problems are assessed. There are several recommendations are provided to reduce the effect of human error so as to increase production by careful consideration of maintenance activities. .

  • 46.
    Kans, Mirka
    et al.
    Linnaeus University.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Thaduri, Adithya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Maintenance 4.0 in Railway Transportation Industry2016Ingår 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. 317-331Konferensbidrag (Refereegranskat)
    Abstract [en]

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

  • 47.
    Karim, Ramin
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Westerberg, Jesper
    eMaintenance365 AB.
    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.
    Maintenance Analytics: The New Know in Maintenance2016Ingår i: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 49, nr 28, s. 214-219Artikel i tidskrift (Refereegranskat)
    Abstract [en]

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

  • 48.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Methodology for the Estimation of the Fatigue Life of Rolling Element Bearings in Non-stationary Conditions2016Ingår i: Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the Fourth International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO'2014, Lyon, France December 15-17 / [ed] Fakher Chaari; Radozlaw Zimroz; Walter Bertelmus; Mahamed Haddar, Cham: Encyclopedia of Global Archaeology/Springer Verlag, 2016, Vol. 4, s. 413-423Konferensbidrag (Refereegranskat)
    Abstract [en]

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

  • 49.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Multi-body modelling of rolling element bearings and performance evaluation with localised damage2016Ingår i: Eksploatacja i Niezawodnosc - Maintenance and Reliability, ISSN 1507-2711, Vol. 18, nr 4, s. 638-648Artikel i tidskrift (Refereegranskat)
    Abstract [en]

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

  • 50.
    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.

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