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  • 1.
    Ahmadi, Alireza
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Gupta, Suprakash
    Banaras Hindu University, Varanasi.
    Ghodrati, Behzad
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Estimation of economic consequences of aircraft system failures2012In: Communications in Dependability and Quality Management, ISSN 1450-7196, Vol. 15, no 1, p. 39-49Article in journal (Refereed)
    Abstract [en]

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

  • 2.
    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 University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    A proposed maintenance strategy for a wind turbine gearbox using condition monitoring techniques2016In: International Journal of Process Management and Benchmarking, ISSN 1460-6739, E-ISSN 1741-816X, Vol. 6, no 3, p. 386-403Article in journal (Refereed)
    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

  • 3.
    Baglee, David
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Knowles, Michael
    University of Sunderland.
    Morris, Adrian
    University of Sunderland.
    O´Hagan, Geraldine
    Glenmorangie Company.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Optimisation of food and engineering supply chain technology (OPTFEST): a case study2013In: International Congress of Condition Monitoring and Diagnostic Engineering Management: Comadem 2013 / [ed] Antti Heijo, Helsinki: KP-Media Oy Messuaukio 1 00520 Helsinki Finland , 2013, p. 498-503Conference paper (Refereed)
    Abstract [en]

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

  • 4.
    Berges, Luis
    et al.
    Department of Design Engineering and Manufacturing, University of Zaragoza.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Stenström, Christer
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Qualitative and quantitative aspects of maintenance performance measurement: a data fusion approach2013In: International Journal of Strategic Engineering Asset Management (IJSEAM), ISSN 1759-9733, E-ISSN 1759-9741, Vol. 1, no 3, p. 238-252Article in journal (Refereed)
    Abstract [en]

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

  • 5.
    Björling, Sten-Erik
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Baglee, David
    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.
    Singh, Sarbjeet
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Maintenance knowledge management with fusion of CMMS and CM2013In: DMIN 2013 International Conference on Data Mining: 22nd -25th July 2013, Las Vegas, Nevada, USA, 2013Conference paper (Refereed)
    Abstract [en]

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

  • 6.
    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 University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Jha, Prakash C.
    Department of Operational Research, University of Delhi, India.
    A multi-objective fuzzy mathematical approach for sustainable reverse supply chain configuration2017In: Journal of Transport and Supply Chain Management, ISSN 2310-8789, E-ISSN 1995-5235, Vol. 11, article id a267Article in journal (Refereed)
    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.

  • 7.
    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 University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. 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 4.02018In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 50, p. 92-111Article in journal (Refereed)
    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.

  • 8.
    Famurewa, Stephen Mayowa
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Asplund, Matthias
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Implementation of performance based maintenance contracting in railway industries2013In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 4, no 3, p. 231-240Article in journal (Refereed)
    Abstract [en]

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

  • 9.
    Famurewa, Stephen Mayowa
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Stenström, Christer
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Asplund, Matthias
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Composite indicator for railway infrastructure management2013Conference paper (Refereed)
    Abstract [en]

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

  • 10.
    Famurewa, Stephen Mayowa
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Stenström, Christer
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Asplund, Matthias
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Composite indicator for railway infrastructure management2014In: Journal of Modern Transportation, ISSN 2095-087X, E-ISSN 2196-0577, Vol. 22, no 4, p. 214-224Article in journal (Refereed)
    Abstract [en]

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

  • 11.
    Farinha, José Manuel Torres
    et al.
    CEMUC® – University of Coimbra's Mechanical Engineering Research Center, University of Coimbra.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Fonseca, Inácio Adelino
    CEMUC® – University of Coimbra's Mechanical Engineering Research Center, University of Coimbra.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Certification of maintenance providers: a competitive advantage2013In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 19, no 2, p. 144-156Article in journal (Refereed)
    Abstract [en]

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

  • 12.
    Fornlöf, Veronica
    et al.
    University of Skövde.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Syberfeldt, Anna
    University of Skövde.
    Almgren, torgny
    GKN Aerospace Engine Systems, Trollhättan.
    On-Condition Parts Versus Life Limited Parts: A Trade off in Aircraft Engines2016In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar; Alireza Ahmadi; Ajit Kumar Verma; Prabhakar Varde, Encyclopedia of Global Archaeology/Springer Verlag, 2016, p. 253-262Conference paper (Refereed)
    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.

  • 13.
    Fuqing, Yuan
    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.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    A comparative study of artificial neural networks and support vector machine for fault diagnosis2013In: International Journal of Pedagogy, Innovation and New Technologies, ISSN 0973-1318, E-ISSN 2392-0092, Vol. 9, no 1, p. 49-60Article in journal (Refereed)
    Abstract [en]

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

  • 14.
    Fuqing, Yuan
    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.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    An adaptive multiple kernel method-based support vector machine used for classication2013In: International Journal of Condition Monitoring, ISSN 0019-6398, E-ISSN 2047-6426, Vol. 3, no 1, p. 8-15Article in journal (Refereed)
    Abstract [en]

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

  • 15.
    Fuqing, Yuan
    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.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Failure detection using support vector machine and artificial neural networks: a comparative study2011In: The Eighth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies: St. David's Hotel, Cardiff, Wales, 20 - 22 June 2011 ; CM2011/MFPT2011, Longborough, Glos.: Coxmoor Publishing Co. , 2011, Vol. 1, p. 189-201Conference paper (Refereed)
  • 16.
    Fuqing, Yuan
    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.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Failure diagnosis of railway assets using support vector machine and ant colony optimization method2012In: International Journal of COMADEM, ISSN 1363-7681, Vol. 15, no 2, p. 3-10Article in journal (Refereed)
    Abstract [en]

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

  • 17.
    Fuqing, Yuan
    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.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering.
    Reliability prediction using support vector regression2010In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 1, no 3, p. 263-268Article in journal (Refereed)
    Abstract [en]

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

  • 18.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Editorial Special Issue on Developments and Applications in Maintenance Performance2013In: International Journal of Strategic Engineering Asset Management (IJSEAM), ISSN 1759-9733, E-ISSN 1759-9741, Vol. 1, no 3, p. 225-227Article in journal (Refereed)
  • 19.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Reliability and maintenance programs in nuclear power plants2011Conference paper (Refereed)
  • 20.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    University of Zaragoza.
    Application of dynamic benchmarking of rotating machinery for eMaintenance2010In: Proceedings of the 1st international workshop and congress on eMaintenance, Luleå tekniska universitet, 2010, p. 227-233Conference paper (Refereed)
    Abstract [en]

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

  • 21.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    Universidad de Zaragoza.
    Lambán, Ma Pilar
    Universidad de Zaragoza.
    Tormos, Bernardo
    Universidad Politécnica de Valencia.
    The measurement of maintenance function efficiency through financial KPIS2014In: Dyna, ISSN 0012-7353, Vol. 81, no 184, p. 102-109Article in journal (Refereed)
    Abstract [en]

    The measurement of the performance in the maintenance function has produced large sets of indicators that due to their nature and disparity in criteria and objectives have been grouped in different subsets lately, emphasizing the set of financial indicators. To generate these indicators properly is necessary to have accurate input data. Hence in this paper we propose a comprehensive model of consensus between the different stakeholders involved in the maintenance function. This will bring about the accurate determination of the maintenance costs of an organization

  • 22.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    University of Zaragoza.
    Lambán, Pilar
    University of Zaragoza.
    Huertas-Talón, Jose Luis
    University of Zaragoza.
    Tormos, Bernardo
    UPV.
    Cálculo de la vida útil remanente mediante trayectorias móviles entre hiperplanos de máquinas de de soporte vectorial2013In: Interciencia, ISSN 0378-1844, Vol. 38, no 8, p. 556-562Article in journal (Refereed)
    Abstract [en]

    We propose a new method for predicting remaining useful life ( RUL ) classifiers inspired by support vector machines ( SVM). Historical data of condition of a system during its lifetime are used to create a classification by hyperplanes in SVM. To estimate the RUL of a system , the degradation rate was assessed by calculating the minimum distance defined based on the degradation pathways , ie , the progressive approach hyperplane segregates information from both good and bad for different time horizons.One can estimate the final life of a specific component , or the information in the RUL of a population be calculated by aggregating multiple RUL estimates using a density estimation method . The degradation of a system is affected by many unknown factors , besides complicating degradation behaviors , difficult to collect quality data.Due to lack of knowledge and incomplete measurements , normally lacks important information on the context of the data collected . Therefore historical data of the system with a variety of degradation patterns are grouped , with the search for a global model RUL prediction is extremely difficult. This leads them to seek advanced forecasting techniques beyond traditional models. The proposed model develops an effective RUL prediction method that addresses multiple challenges in forecasting complex systems. The similarities between degradation pathways can be contrasted to enrich existing methodologies prognosis.

  • 23.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    University of Zaragoza.
    Lambán, Pilar
    University of Zaragoza.
    Tormos, Bernardo
    UPV.
    La medición de la eficiencia de la función mantenimiento a través de KPIs financieros2014In: Dyna, ISSN 0012-7353, Vol. 81, no 184, p. 102-109Article in journal (Refereed)
    Abstract [en]

    The measurement of the performance in the maintenance function has produced large sets of indicators that due to their nature and disparity in criteria and objectives have been grouped in different subsets lately, emphasizing the set of financial indicators. To generate these indicators properly is necessary to have accurate input data. Hence in this paper we propose a comprehensive model of consensus between the different stakeholders involved in the maintenance function. This will bring about the accurate determination of the maintenance costs of an organization.

  • 24. Galar, Diego
    et al.
    Berges, Muro, L.
    Department of Engineering Design and Manufacture, School of Engineering, University of Zaragoza.
    Royo, Javier
    Manufacturing Engineering and Advanced Metrology Group, Aragon Institute of Engineering Research (13A), University of Zaragoza.
    RAMS parameters as KPI rotating machinery maintenance2010In: COMADEM 2010: advances in maintenance and condition diagnosis technologies towards sustainable society : proceedings of the 23rd international congress on condition monitoring and diagnostic engineering management, June 28-July2, 2010, Nara, Japan / [ed] Susumu Okumura, Hikone: Sunrise Publishing , 2010, p. 851-858Conference paper (Refereed)
    Abstract [en]

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

  • 25.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Fuqing, Yuan
    Department of Engineering and Safety, University of Tromsø, Tromsø, Norway.
    Harmonic and Inter-harmonic Analysis on Power Signal from Railway Traction Systems2017In: International Journal of COMADEM, ISSN 1363-7681, Vol. 20, no 2, p. 3-10Article in journal (Refereed)
    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.

  • 26.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ghodrati, Behzad
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Fusion of CMMS Data and CM Data - a real need for maintenance: Part 22012In: Maintworld, ISSN 1798-7024, E-ISSN 1799-8670, no 3, p. 40-43Article in journal (Other academic)
  • 27.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ghodrati, Behzad
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Fusion of CMMS data and CM data: a real need for maintenance (part I)2012In: Maintworld, ISSN 1798-7024, E-ISSN 1799-8670, no 2, p. 42-45Article in journal (Other academic)
    Abstract [en]

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

  • 28.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Gustafson, Anna
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Tormos, Bernardo
    Berges, Luis
    Podejmowanie decyzji eksploatacyjnych w oparciu o fuzje{ogonek} różnego typu danych2012In: Eksploatacja i niezawodnosc - Maintenance and Reliability, ISSN 1507-2711, Vol. 14, no 2, p. 135-144Article in journal (Refereed)
    Abstract [en]

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

  • 29.
    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)
  • 30.
    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.

  • 31.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kans, Mirka
    Department of Mechanical Engineering, Linnaeus University, Växjö.
    The Impact of Maintenance 4.0 and Big Data Analytics within Strategic Asset Management2017In: Proceedings of MPMM 2016: 6th International Conference on Maintenance Performance Measurement and Management, 28 November 2016, Luleå, Sweden / [ed] Diego Galar, Dammika Seneviratne, Luleå: Luleå tekniska universitet, 2017, p. 96-104Conference paper (Refereed)
    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.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kans, Mirka
    Linnaeus University.
    Schmidt, Bernard
    University of Skövde.
    Big Data in Asset Management: Knowledge Discovery in Asset Data by the Means of Data Mining2016In: Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015) / [ed] Kari T. Koskinen; Helena Kortelainen; Jussi Aaltonen; Teuvo Uusitalo; Kari Komonen; Joseph Mathew; Jouko Laitinen, Encyclopedia of Global Archaeology/Springer Verlag, 2016, p. 161-171Conference paper (Refereed)
    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.

  • 33.
    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.
    SMART bearing: from sensing to actuation2013In: Proceedings of the 12th IMEKO TC10 Workshop on Technical Diagnostics: New Perspectives in Measurements, Tools and Techniques for Industrial Applications, Florence, Italy: IMEKO , 2013, p. 21-30Conference paper (Refereed)
    Abstract [en]

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

  • 34.
    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.
    Berges, Luis
    Department Design engineering and manufacturing, University of Zaragoza.
    Sandborn, Peter
    CAlCe Center for Advanced life Cycle engineering, University of Maryland.
    The need for aggregated indicators in performance asset management2014In: Eksploatacja i niezawodnosc - Maintenance and Reliability, ISSN 1507-2711, Vol. 16, no 1, p. 120-127Article in journal (Refereed)
    Abstract [en]

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

  • 35.
    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.
    Fuqing, Yuan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    RUL prediction using moving trajectories between SVM hyper planes2012In: 2012 proceedings: Annual Reliability and Maintainability Symposium (RAMS 2011) : Reno, Nv 23-26 Jan. 2012, Piscataway, NJ: IEEE Communications Society, 2012Conference paper (Refereed)
    Abstract [en]

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

  • 36.
    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.
    Juuso, Esko
    University of Oulu.
    Lahdelma, Sulo
    University of Oulu.
    Fusion of maintenance and control data: a need for the process2012In: Proceedings of 18th World Conference on Nondestructive Testing: April 2012, Durban, South Africa, 2012Conference paper (Refereed)
    Abstract [en]

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

  • 37.
    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.
    Lee, J.
    NSFI/UCR Center for Intelligent Maintenance System (IMS), University of Cincinnati.
    Zhao, W.
    NSFI/UCR Center for Intelligent Maintenance System (IMS), University of Cincinnati.
    Remaining useful life estimation using time trajectory tracking and support vector machines2012In: 25th International Congress on Condition Monitoring and Diagnostic Engineering (COMADEM 2012): 18–20 June 2012, Huddersfield, UK, IOP Publishing Ltd , 2012, article id 012063Conference paper (Refereed)
    Abstract [en]

    In this paper, a novel RUL prediction method inspired by feature maps and SVM classifiers is proposed. The historical instances of a system with life-time condition data are used to create a classification by SVM hyper planes. For a test instance of the same system, whose RUL is to be estimated, degradation speed is evaluated by computing the minimal distance defined based on the degradation trajectories, i.e. the approach of the system to the hyper plane that segregates good and bad condition data at different time horizon. Therefore, the final RUL of a specific component can be estimated and global RUL information can then be obtained by aggregating the multiple RUL estimations using a density estimation method.

  • 38.
    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.
    Lee, Jay
    Center for Intelligent Maintenance Systems (IMS), Cincinnati, OH.
    Zhao, Wenyu
    Center for Intelligent Maintenance Systems (IMS), Cincinnati, OH.
    Remaining useful life estimation using time trajectory tracking and support vector machines2012In: International Journal of COMADEM, ISSN 1363-7681, Vol. 15, no 3, p. 2-8Article in journal (Refereed)
    Abstract [en]

    In this paper, a novel RUL prediction method inspired by feature maps and SVM classifiers is proposed. The historical instances of a system with life-time condition data are used to create a classification by SVM hyper planes. To test the system's RUL, degradation speed is evaluated by computing the minimal distance based on the degradation trajectories, i.e. the approach of the system to the hyper plane that segregates good and bad condition data on a different time horizon. The final RUL of a specific component can be estimated and global RUL information can be obtained by aggregating the multiple RUL estimations using a density estimation method

  • 39.
    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.
    Parida, Aditya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Maintenance audits: elements of the metric2012In: Mantenimiento, ISSN 0214-4344Article in journal (Other academic)
  • 40.
    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.
    Parida, Aditya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    University of Saragossa.
    Auditorias de mantenimiento2011In: Ingenieria y Gestion de Mantenimiento, ISSN 1695-3754, Vol. 16, no 76, p. 16-29Article in journal (Refereed)
  • 41.
    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.
    Sandborn, P.
    CAlCe Center for Advanced life Cycle engineering, University of Maryland.
    Morant, Amparo
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    O&M efficiency model: A dependability approach2012In: 25th International Congress on Condition Monitoring and Diagnostic Engineering (COMADEM 2012): 18–20 June 2012, Huddersfield, UK, IOP Publishing Ltd , 2012, article id 012111Conference paper (Refereed)
    Abstract [en]

    The occurrence of equipment failures is one of the main causes of inefficiency. These events increase the operational costs and give rise to a loss of revenues or in the worst case they can even produce an accident with significant damages to people and the environment. The efficiency of the operation of an industrial installation in a given period of time has been defined use the ratio of the Dependability level achieved by the installation in a specified period of time and the sum of the corresponding Dependability and Undependability costs. The aim of this paper is the development of a methodology for the calculation of operating costs in industrial facilities that addresses the difficulty of performing a simple, homogeneous and objective evaluation of the operational efficiency of the industrial facility. The main obstacle to this evaluation is the lack of a method for quantifying Dependability, which to date has always been considered a purely qualitative characteristic.

  • 42.
    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

  • 43.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Morant, Amparo
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Integration of production data in CM for non-stationary machinery: a data fusion approach2012In: Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the Second International Conference "Condition Monitoring of Machinery in Non-Stationnary Operations" CMMNO’2012, Berlin: Springer Berlin/Heidelberg, 2012, p. 403-414Chapter in book (Refereed)
    Abstract [en]

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

  • 44.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Naeem, Hassan Bin
    Schunnesson, Håkan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Berges, Luis
    Department of Design Engineering and Manufacturing, University of Zaragoza.
    Tormos, Bernardo
    CMT-Motores Térmicos, Universitat Politècnica de València.
    Fusion of Operations, Event-Log and Maintenance Data: A Case Study for Optimising Availability of Mining Shovels2014In: Mine Planning and Equipment Selection: Proceedings of the 22nd MPES Conference, Dresden, Germany, 14th – 19th October 2013 / [ed] Carsten Drebenstedt; Raj Singhal, Switzerland: Encyclopedia of Global Archaeology/Springer Verlag, 2014, Vol. IX, p. 1173-1194Conference 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® MineStarTM, 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.

  • 45.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Palo, Mikael
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Horenbeek, Adriaan Van
    Centre for Industrial management, KU Leuven.
    Printelon, Liliane M.
    Centre for Industrial management, KU Leuven.
    Integration of disparate data sources to perform maintenance prognosis and optimal decision making2012In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 54, no 8, p. 440-445Article in journal (Refereed)
    Abstract [en]

    Prognosis can be defined as the course of predicting a failure of equipment or a component in advance, whereas prognostication refers to the act of prediction. The three main branches of condition-based maintenance are diagnosis, prognosis and treatment-prognosis; however, prognosis is admittedly the most difficult. Also, this area has been the least described in literature and the knowledge about it in a maintenance management context is still poorly systematised. To this day, formal professional attention to prognosis, in the field of maintenance management and engineering in the everyday care of machinery, is often relegated to a secondary status, although the availability of prognostic information can considerably improve (for example reduce costs and maximise uptime) the performance of machinery and maintenance processes. Ideally, assessment of a prognosis of remaining useful life should be deliberate and explicit. In order to support the maintenance crew in the achievement of this objective, an increasing amount of prognostic information is available. Over the last decade, system integration has grown in popularity as it allows organisations to streamline business processes. It is necessary to integrate management data from computer maintenance management systems (CMMS) with condition monitoring (CM) systems and finally supervisory control and data acquisition (SCADA) and other control systems, widely used in production but seldom with a usage in asset diagnosis and prognosis. The most obvious obstacle in the integration of these data is the disparate nature of the data types involved; moreover, several attempts to remedy this problem have fizzled out. Although there have been many recent efforts to collect and maintain large repositories of these types of data, there have been relatively few studies to identify the ways these datasets could be related and linked for prognosis and maintenance decision making. After identifying what and how to predict incipient failures and developing a corresponding prognosis, maintenance engineers must consider how to communicate the prediction. In this activity, once again, technicians' psychosocial attributes and values may influence how they discuss prognoses with asset managers. Regardless of whether prognostic assessments are subjective or objective, however, technicians should consider two major points. Firstly, the maintenance crew should clarify in their own minds the link, if any, between their prognostic assessment and their consequent decision making. Secondly, they should consider the ways that they and their assets might benefit from explicitly discussing how the prognostic assessment is linked with diagnostics and preventive maintenance recommendations. These and other steps that maintenance engineers should take in incorporating prognostic information into their decision making are discussed in this paper. The objective is to give an overview of how the integration of disparate data sources, commonly available in industry, can be achieved for maintenance prognosis and optimal decision making.

  • 46.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Parida, Aditya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Gustafson, Anna
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    University of Zaragoza.
    Use of vibration monitoring and analysis as KPIs in paper industry2011In: Proceedings of the 24th International Congress on Condition Monitoring and Diagnosis Engineering Management: COMADEM 2011 / [ed] Maneesh Singh; Raj B.K.N. Rao; J.P. Liyanage, COMADEM International, 2011, p. 135-147Conference paper (Refereed)
    Abstract [en]

    The use of automated systems for monitoring or surveillance of the condition of machinery for an industrial plant is becoming more common. Variables or indicators are transmitted to a database connected "on line" with such systems, allowing us to correctly track the condition parameters included in the PdM (Predictive Maintenance) Program. A practical example of an installation of 500 measuring points in a paper mill is presented in this paper. Paper mill plants show a wide variety of defects and conditions related to problems in rotating machinery bearings, gears, low speed operation, variable speed machine due to harsh conditions in the environment for elements of the installation and other adverse factors. Therefore, the vibration analyses are presented as physical indicators of the technical system for this type of machinery, on which the maintenance performance metrics are built. The article concludes presenting the "state of the art in data transmission to remote facilities engineering, for predictive diagnostic in order to detect potential problems in machinery which facilitate decisionmaking process in maintenance departments. These systems allow the rapid construction of the KPIs at different hierarchical levels. Therefore an approach to decision making based on the Maintenance methodology has been developed as a result of technological advances in the collection of physical data

  • 47.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Parida, Aditya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Baglee, D.
    School of Computing and Technology, University of Sunderland.
    Morant, Amparo
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    The measurement of maintenance function efficiency through financial KPIs2012In: 25th International Congress on Condition Monitoring and Diagnostic Engineering (COMADEM 2012): 18–20 June 2012, Huddersfield, UK, IOP Publishing Ltd , 2012, article id 012112Conference paper (Refereed)
    Abstract [en]

    The measurement of the performance in the maintenance function has produced large sets of indicators that due to their nature and disparity in criteria and objectives have been grouped in different subsets lately, emphasizing the set of financial indicators. The generation of these indicators demands data collection of high reliability that is only made possible through a model of costs adapted to the special casuistry of the maintenance function, characterized by the occultism of these costs.

  • 48.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Parida, Aditya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Stenström, Christer
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    Manufacturing Engineering and Advanced Metrology Group, Aragon Institute of Engineering Research (13A), University of Zaragoza.
    Maintenance metrics: a hierarchical model of balanced scorecard2011In: 2011 IEEE International Conference on Quality and Reliability: ICQR 2011 : Bangkok, 14 September 2011-17 September 2011, Piscataway, NJ: IEEE Communications Society, 2011, p. 67-74Conference paper (Refereed)
    Abstract [en]

    The system of performance measurement of maintenance function should cover all processes related to it within the organization. There must be an interconnection between the different indicators, so the numbers can be interpreted in order to reach a good conclusion for decision making. This premise implies a hierarchy of indicators needed in a dual way. First, it will require maintenance indicators to be segmented according to the areas of influence for the rest of the organization, posed by interactions with finance department, human resources, purchasing, and, of course, with production in the seeking of compliance with corporate objectives. Simultaneously, these indicators correspond to different levels in the organization and therefore they will be segmented according to the hierarchical position of end users.

  • 49.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Parida, AdityaLuleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.Schunnesson, HåkanLuleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.Kumar, UdayLuleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    MPMM 2011: Maintenance Performance Measurement & Management: Conference Proceedings2011Collection (editor) (Other academic)
  • 50.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Peters, Ralph
    Maintenance Excellence Institute.
    Berges, Luis
    Manufacturing Engineering and Advanced Metrology Group, Aragon Institute of Engineering Research (13A), University of Zaragoza.
    Stenström, Christer
    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.
    Composite indicators in asset management2012Conference paper (Refereed)
    Abstract [en]

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

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