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  • 1.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Johansson, Carl-Anders
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Mouzoune, A.
    Mohammed V University.
    Taibi, S.
    Mohammed V University.
    Risk Based Maintenance policies for SMART devices2013In: Proceedings of International Conference Life Cycle Engineering and Management ICDQM-2013 / [ed] Ljubisa Papic, Prijevor: Research Center of Dependability and Quality Management DQM , 2013, Vol. 2, p. 470-486Conference paper (Refereed)
  • 2.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Johansson, Carl-Anders
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Mouzoune, A.
    Ecole Mohammadia d'ingénieurs, Mohammed V University – Agdal, Rabat.
    Taibi, S.
    Ecole Mohammadia d'ingénieurs, Mohammed V University – Agdal, Rabat.
    Risk Based Maintenance policies for SMART devices2013In: Communications in Dependability and Quality Management, ISSN 1450-7196, Vol. 16, no 3, p. 15-28Article in journal (Refereed)
    Abstract [en]

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

  • 3.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Villarejo, Roberto
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Johansson, Carl-Anders
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Hybrid prognosis for railway health assessment: an information fusion approach for PHM deployment2013In: PHM2013: 2013 Prognostic and System Health Management: Milan 8-11 September 2013 / [ed] Enrico Zio; Piero Baraldi, AIDIC Servizi S.r.l. , 2013, p. 769-774Conference paper (Refereed)
    Abstract [en]

    Many railway assets suffer increasing wear and tear during operation. Prognostics can assist diagnosis by assessing the current health of a system and predicting its remaining life based on features that capture the gradual degradation in a system's operational capabilities. Prognostics are critical to improve safety, plan successful work, schedule maintenance, and reduce maintenance costs and down time. Unlike fault diagnosis, prognosis is a relatively new area, but it has become an important part of Condition-based Maintenance (CBM) of systems. As there are many prognostic techniques, usage must be attuned to particular applications. Broadly stated, prognostic methods are either data-driven or model-based. Each has advantages and disadvantages; consequently, they are often combined in hybrid applications. A approach hybrid model can combine some or all model types (data-driven, and phenomenological); thus, more complete information can be gathered, leading to more accurate recognition of the fault state. This approach is especially relevant in railway systems where the maintainer and operator know some of the failure mechanisms, but the complexity of the infrastructure and rolling stock is huge that there is no way to develop a complete model-based approach. Therefore, hybrid models are extremely useful for accurately estimating the Remaining Useful Life (RUL) of railway systems. The paper addresses the process of data aggregation into a hybrid model to get RUL values within logical confidence intervals so that the life cycle of railway assets can be managed and optimised

  • 4.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Sandborn, Peter
    University of Maryland.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Johansson, Carl-Anders
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    SMART - Integrating human safety risk assessment with asset integrity2013In: Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the third International Conference on Condition Monitoring of Machinery in Non-Stationary Operations CMMNO 2013 / [ed] Giorgio Dalpiaz; Riccardo Rubini; Gianluca DÉlia; Marco Cocconcelli; Fakher Chaari; Radoslaw Zimroz; Walter Bartelmus; Mohamed Haddar, Encyclopedia of Global Archaeology/Springer Verlag, 2013, p. 37-59Conference paper (Refereed)
    Abstract [en]

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

  • 5.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Villarejo, Roberto
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Johansson, Carl-Anders
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    Zaragoza University (Higher Polytechnic Centre), Division of Design and Production Engineering.
    Hybrid models for PHM deployment techniques in railway2013In: 10th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2013, CM 2013 and MFPT 2013, 2013, Vol. 2, p. 1047-1056Conference paper (Refereed)
    Abstract [en]

    Many railway assets exhibit increasing wear and tear of equipment during operation. Prognostics are viewed as an add-on capability to diagnosis; they assess the current health of a system and predict its remaining life based on features that capture the gradual degradation in the operational capabilities of a system. Prognostics are critical to improve safety, plan successful missions, schedule maintenance, reduce maintenance cost and down time. Unlike fault diagnosis, prognosis is a relatively new area and became an important part of Condition-based Maintenance (CBM) of systems. Currently, there are many prognostic techniques; their usage must be tuned for each application. The prognostic methods can be classified as being associated with one or more of the following two approaches: data-driven and model-based. Each of these approaches has its own advantages and disadvantages, and consequently, they are often used in combination in many applications called hybrid. A hybrid model could combine some or all of model types (data-driven, and phenomenological), so that more complete information allows for more accurate recognition of the fault state. This approach is especially relevant in railway where the maintainer and operator know some of the failure mechanisms, but the complexity of the infrastructure and rolling stock is huge so no way to develop a complete model based approach that is why development of hybrid models becomes necessary to estimate RUL of railway systems in a more accurate way. The paper address this process of data aggregation into the hybrid model in order to get RUL values within logical confidence intervals so railway assets life cycle can be managed and optimized.

  • 6.
    Johansson, Carl-Anders
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Villarejo, Roberto
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Monnin, Maxime
    PREDICT.
    Green Condition based Maintenance - an integrated system approach for health assessment and energy optimization of manufacturing machines.2013In: 10th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2013, CM 2013 and MFPT 2013, 2013, Vol. 2, p. 1069-1084Conference paper (Refereed)
    Abstract [en]

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

  • 7.
    Johansson, Carl-Anders
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Simon, Victor
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Aggregation of electric current consumption features for extraction of maintenance KPIs2014In: Proceedings of Maintenance Performance Measurement and Management (MPMM) Conference 2014 / [ed] José Torres Farinha; Diego Galar, Coimbra: Faculdade de Ciências e Tecnologia da Universidade de Coimbra, Departamento de Engenharia Mecânica , 2014, p. 157-162Conference paper (Refereed)
    Abstract [en]

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

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    FULLTEXT01
  • 8.
    Johansson, Carl-Anders
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Simon, Victor
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Context Driven Remaining Useful Life Estimation2014In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 22, p. 181-185Article in journal (Refereed)
    Abstract [en]

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

  • 9. Naeem, Hassan Bin
    et al.
    Mainali, Ganesh
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Johansson, Carl-Anders
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Fusion of operations, event-log and maintenance data: a case study for optimising availability of mining shovels2013In: 10th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2013, CM 2013 and MFPT 2013, 2013, Vol. 1, p. 484-503Conference paper (Refereed)
    Abstract [en]

    The modern mining industry is highly mechanised and relies on massive, multimillion-dollar pieces of equipment to achieve production targets. In an increasingly challenging international economic climate, mining operations are reliant on economies of scale to remain competitive. To maximise revenue, it is imperative that at each stage of the mining process, equipment is operating optimally without preventable and unnecessary interruptions. As a result, the focus of all mining operations is to increase equipment uptime and utilisation.The data used for this investigation have been sourced from the Aitik mine, a large open pit copper mine in Northern Sweden. In the loading area, power shovels load trucks with blasted material for transport, either to the crushers or to the waste dumps. The Aitik mine employs various computer-aided applications to track and maintain mobile mining equipment like the shovels. These applications also serve as chronological operational and maintenance databases for the equipment. This paper’s study of six mining shovels is based on the analysis of three data types: historical maintenance data from CMMS Maximo, operational data from mine management system Cat® MineStar™, and event-log data from individual shovels.The results indicate that such a synthesis is viable. A regular time-lapse integration of the diverse data types displays potential and could prove helpful in achieving overall improvements in maintenance.

  • 10.
    Prado, Augustin
    et al.
    GORATU M-H.
    Alzaga, Aitor
    IK4-TEKNIKER.
    Konde, Egoitz
    IK4-TEKNIKER.
    Medina-Oliva, Gabriela
    PREDICT.
    Monnin, Maxime
    PREDICT.
    Johansson, Carl-Anders
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Euhus, Dirk
    ARTIS.
    Burrows, Mike
    MONITION.
    Yurre, Carlos
    FAGOR AOTEK.
    Health and Performances Machine Tool Monitoring Architecture2014In: Proceedings of the 3rd international workshop and congress on eMaintenance: June 17-18 Luleå, Sweden : eMaintenance, Trends in technologies & methodologies, challenges, possibilites and applications / [ed] Uday Kumar; Ramin Karim; Aditya Parida; Philip Tretten, Luleå: Luleå tekniska universitet, 2014, p. 139-144Conference paper (Refereed)
    Abstract [en]

    In order to face the high market competiveness, the Power-OMproject (http://power-om.eu/) aims at implementing a proactiveapproach for improving the machine tool performances. Forimplementing a proactive approach that helps monitoring machinetool performances, this paper presents a technical architecturewith two levels: the local and the remote one. In the local level,condition based maintenance strategy is implemented and realtime data is used for monitoring the local health of a machine. Afirst originality is to use the current analysis for assessing thehealth status of the machine. In the remote level, offline data isstored in an eMaintenance platform, which allows providing afleet dimension. This dimension allows to benefit of more dataand information allowing to make performances comparisonacross the fleet and along the time.This paper presents the advantages of the added-valuearchitecture. On one hand, there is the possibility to trackperformances to detect drifts locally and real-time based on thecurrent analysis, and on the other hand, to follow-up short andmid-term performances for deeper analysis of the fleetperformances in order to bring relevant information for decisionmakers.

    Download full text (pdf)
    FULLTEXT01
  • 11.
    Saari, Juhamatti
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Mishra, Madhav
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Johansson, Carl-Anders
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Applied methods of condition monitoring and fault detection for underground mobile machines2013Conference paper (Refereed)
    Abstract [en]

    Condition monitoring is a health assessment technique worldwide accepted and very popular in many industries especially where there are rotating machines involved in the processes. These techniques may be relevant in environments where the prediction of a failure and the prevention and mitigation of its consequences increase the profit and safety of the facilities. The maintenance of underground mobile mining equipment is one of these scenarios. It has several problem areas: harsh environment, potential risks and distant location of workshops. When a machine breaks down, there are two ways to handle the repair. Either the equipment has to be repaired on site at the production area or taken to the workshop. The difficulties involved in moving this type of large equipment are substantial but it might be difficult or unsafe to repair the LHD on site (depending on where and why it fails). Therefore it is necessary to identify the critical components and monitor them properly in order to skip undesired shutdowns or stoppages. This paper describes the benefits of different CM techniques applied to a critical part of a LHD machine (the transmission) in order to detect the abnormal behavior if any, identify the fault and predict the degradation. These techniques will provide enough information to optimize the maintenance actions minimizing and mitigating the costly effects of unplanned actions.

  • 12.
    Villarejo, Roberto
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Johansson, Carl-Anders
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Menendez, Manuel
    Vias y Construcciones S.A..
    Perales, Numan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Context Awareness And Railway Maintenance2014In: Proceedings of the 3rd international workshop and congress on eMaintenance: June 17-18 Luleå, Sweden : eMaintenance, Trends in technologies & methodologies, challenges, possibilites and applications / [ed] Uday Kumar; Ramin Karim; Aditya Parida; Philip Tretten, Luleå: Luleå tekniska universitet, 2014, p. 17-24Conference paper (Refereed)
    Abstract [en]

    A railway is an extremely complex system requiring maintenancedecision support systems to gather data from many disparatesources. These sources include traditional maintenanceinformation like condition monitoring or work records, as well astraffic information, given the criticality of maintenance inavoiding traffic disruptions and the need to minimise the trackpossession time for maintenance.A methodology is required if maintainers are to understand thedata as a whole. Context engines try to link the various dataconstellations and to define interactions within the railwaysystem. This is not easy since data have different natures, originsand granularity. But if all information surrounding the railwayasset can be considered, decisions will be more accurate andproblems like false alarms or outlying anomalies will be detected.The contextualisation of the data seems to be a feasible way toallow condition monitoring data i.e physical measurements andother variables, to be understood under certain conditions(weather, regulations etc.) and as a consequence of certain actions(maintenance interventions, overhauls, outsourcing warrantiesetc.).This paper proposes the use of context engines to providemeaningful information out of the overwhelming amount ofcollected and recorded data so that proper maintenance decisionscan be made. In this scenario, fluffy information coming fromwork orders and expertise of maintainers is a big issue since suchinformation must be converted to numerical values. The fuzzylogic approach seems a promising way to integrate suchinformation sources for diagnosis.

    Download full text (pdf)
    FULLTEXT01
  • 13.
    Villarejo, Roberto
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Johansson, Carl-Anders
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Sandborn, Peter
    University of Maryland, Department of Mechanical Engineering.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Context-driven decisions for railway maintenance2016In: Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, ISSN 0954-4097, E-ISSN 2041-3017, Vol. 230, no 5, p. 1469-1483Article in journal (Refereed)
    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.

  • 14.
    Villarejo, Roberto
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Johansson, Carl-anders
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Urko, Leturiondo
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4 Ikerlan, P J Ma Arizmendiarrieta 2, Arrasate Mondragon 20500, Spain.
    Simon, Victor
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Seneviratne, Dammika
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Bottom to Top Approach for Railway KPI Generation2017In: Management Systems in Production Engineering, ISSN 2299-0461, Vol. 25, no 3, p. 191-198, article id 28Article in journal (Refereed)
    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.

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