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  • 51.
    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, Cincinnati, OH 45221, USA.
    Zhao, W.
    NSFI/UCR Center for Intelligent Maintenance System (IMS), University of Cincinnati, Cincinnati, OH 45221, USA.
    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, Vol. 364, 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.

  • 52.
    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, United States.
    Zhao, Wenyu
    Center for Intelligent Maintenance Systems (IMS), Cincinnati, OH, United States.
    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.

  • 53.
    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)
  • 54.
    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)
  • 55.
    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.

  • 56.
    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

  • 57.
    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

  • 58.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Naeem, Hassan Bin
    Luleå University of Technology.
    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, 50018 Zaragoza, Spain.
    Tormos, Bernardo
    CMT-Motores Térmicos, Universitat Politècnica de València, 46022 Valencia, Spain.
    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.

  • 59.
    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.
    Van Horenbeek, Adriaan
    Centre for Industrial management, KU Leuven.
    Pintelon, Liliane M.
    Centre for Industrial management, KU Leuven.
    Integration of disparate data sources to perform maintenance prognosis and optimal decision making2012In: Insight: Non-Destructive Testing & Condition Monitoring, 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.

  • 60.
    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

  • 61.
    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.

  • 62.
    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.

  • 63.
    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)
    Download full text (pdf)
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  • 64.
    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

  • 65.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Pilar, Lamban
    Manufacturing and Design Engineering Department, University of Zaragoza.
    Luis, Berges
    Manufacturing and Design Engineering Department, University of Zaragoza.
    Application of dynamic benchmarking of rotating machinery for e-maintenance2010In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 1, no 3, p. 246-262Article in journal (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, operational or environmental conditions in which the machine is analyzed. It creates false alarms and many unnecessary interventions. New communication technologies allow the integration of e-maintenance 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. Absolute vibration data and spectral analysis of rotating machinery require the study of several signals by machine, which become hundreds of values and spectra to analyze where there, is a large number of machines. It is therefore necessary to find proper benchmark points to compare with vibration parameters. These parameters and benchmark points have to be adapted to the real status of the plant and vibratory conditions have to be automated to be easily understood by persons not connected with the detailed analysis of spectra. The trend of the measured data and its comparison with benchmarks should assess the success of the implementation of CBM and other decisions about implementation and changes in maintenance programs. This article proposes the use of two new indicators that result from data mining as a reference dynamic, not static as proposed by the standard, manufacturer or the expertise of maintenance technicians. These values show the real condition of the machine in terms of vibration. The application of these references to the decision making process of the maintenance manager and its inclusion in maintenance scorecard 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 e-maintenance system

  • 66.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Sandborn, Peter
    ALCE Electronic Products and Systems Center, University of Maryland, College Park, Maryland, USA.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Maintenance Costs and Life Cycle Cost Analysis2017Book (Refereed)
  • 67.
    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.

  • 68.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Seneviratne, DammikaLuleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Management Systems in Production Engineering: Maintenance Performance Measurement and Management Challenges:  From Sensing to Decision Support2017Collection (editor) (Other academic)
  • 69.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Seneviratne, DammikaLuleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    MPMM 2016, Maintenance, Performance, Measurement & Management: conference proceedings2017Conference proceedings (editor) (Refereed)
    Abstract [en]

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

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

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

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

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

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

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

    Download full text (pdf)
    fulltext
  • 70.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Spain.
    Seneviratne, Dammika
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Spain.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Big Data in Railway O&M: A Dependability Approach2022In: Research Anthology on Big Data Analytics, Architectures, and Applications, IGI Global, 2022, Vol. 1, p. 391-416Chapter in book (Other academic)
    Abstract [en]

    Railway systems are complex with respect to technology and operations with the involvement of a wide range of human actors, organizations and technical solutions. For the operations and control of such complexity, a viable solution is to apply intelligent computerized systems, for instance, computerized traffic control systems for coordinating airline transportation, or advanced monitoring and diagnostic systems in vehicles. Moreover, transportation assets cannot compromise the safety of the passengers by only applying operation and maintenance activities. Indeed, safety is a more difficult goal to achieve using traditional maintenance strategies and computerized solutions come into the picture as the only option to deal with complex systems interacting among them and trying to balance the growth in technical complexity together with stable and acceptable dependability indexes. Big data analytics are expected to improve the overall performance of the railways supported by smart systems and Internetbased solutions. Operation and Maintenance will be application areas, where benefits will be visible as a consequence of big data policies due to diagnosis and prognosis capabilities provided to the whole network of processes. This chapter shows the possibilities of applying the big data concept in the railway transportation industry and the positive effects on technology and operations from a systems perspective. © 2022 by IGI Global. All rights reserved.

  • 71.
    Galar, Diego
    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.
    Auditoria de Manutenção Baseada em Elementos Quantitativos e Qualitativos em Sistemas de Saúde: [Maintenance Audit Based on Quantitative and Qualitative Elements for Health Care Systems]2011In: Tecno Hospital, ISSN 1645-9431, no 47, p. 24-29Article in journal (Other academic)
    Abstract [en]

    The dependability of hospital facilities and equipments is a critical element in the performance of health care systems. The availability needs to be near one hundred percent, especially equipment related to the emergency department. Faults in equipments have to be rectified as fast as possible, i.e. the organizational readiness and the maintainability of the equipments need to be excellent. This paper introduces a maintenance audit model, based on quantitative and qualitative elements, together with a maturity model for facilities and equipments of health care systems. Qualitative and quantitative methods are combined in order to complement advantages and disadvantages of them both.

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  • 72.
    Galar, Diego
    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.
    Parida, Aditya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Rupesh
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    University of Zaragoza.
    Human factor in maintenance performance measurement2011In: IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Piscataway, NJ: IEEE Communications Society, 2011, p. 1569-1576Conference paper (Refereed)
    Abstract [en]

    The maintenance performance measurement is often faced with a lack in knowledge about the real function of the maintenance department within organizations, and consequently the absence of appropriate targets emanating from the global mission and vision. These facts bring about metrics not adapted to the real needs, which has a strong load of human factor and without a roadmap of the amount of data to be collected, their processing and use in decision making. This article proposes a model where qualitative and quantitative methods are combined in order to complement advantages and disadvantages of them both.

  • 73.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Thaduri, Adithya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Catelani, Marcantonio
    Department of Information Engineering, University of Florence.
    Ciani, Lorenzo
    Department of Information Engineering, University of Florence.
    Context awareness for maintenance decision making: A diagnosis and prognosis approach2015In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 67, p. 137-150Article in journal (Refereed)
    Abstract [en]

    All assets necessarily suffer wear and tear during operation. Prognostics can assess the current health of a system and predict its remaining life based on features capturing 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 but has become an important part of Condition-based Maintenance (CBM) of systems. Broadly stated, prognostic methods are either data-driven, rule based, or model-based. Each approach has advantages and disadvantages; 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 fault state. In this context, it is important to evaluate the consistency and reliability of the measurement data obtained during laboratory testing and the prognostic/diagnostic monitoring of the system under examination.This approach is especially relevant in systems where the maintainer and operator know some of the failure mechanisms with a sufficient amount of data, but the sheer complexity of the assets precludes the development of a complete model-based approach. This paper addresses the process of data aggregation into a contextual awareness hybrid model to get Residual Useful Life (RUL) values within logical confidence intervals so that the life cycle of assets can be managed and optimised.

  • 74.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Thaduri, Adithya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Pascual, Rodrigo
    Pontificia Universidad Católica de Chile.
    SMART maintenance and prescriptive asset management for mining2016Conference paper (Refereed)
    Abstract [en]

    Operation and maintenance (O&M) 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 behaviour. Indeed, SMARTness is one step beyond the prediction of failure time but also a proposition of operation and maintenance profiles in order to fulfill the company goals. Therefore prognosis and RUL estimation become a part of the process in order to achieve prescriptive actions and control the degradation and operational aspects of the asset as per expected demand and customer request. These O&M decisions must be made on the basis of accepted risk. Performed or unperformed 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 the SMARTness of assets in order to go one step forwards and propose prescriptive O&M decisions based on a self-risk assessment as a trade-off for asset integrity and company goals.

  • 75.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Thaduri, Adithya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Simon, Victor
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Catelani, Marcantonio
    Department of Information Engineering, University of Florence.
    Ciani, Lorenzo
    Department of Information Engineering, University of Florence.
    Prognostic Hybrid Modelling from Data Fusion on Machine Tools2016In: Measurement, ISSN 1536-6367, E-ISSN 1536-6359Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

    This paper proposes an enhancement of remaining useful life (RUL) prediction method based on degradation trajectory tracking under the scope of machine tools. The operational condition data of the machine over time provides the potential degradation state at the next estimation iteration step, based on data-driven techniques. The model-based approach is considered as long-term prognostics method assuming that a physical model describing the degradation behaviour is available. Fusing the aforementioned techniques outputs a hybrid model for RUL estimation.

  • 76.
    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.

  • 77.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Wandt, Karina
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Karim, Ramin
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    Department of Design Engineering and Manufacturing, University of Zaragoza.
    The evolution from e(lectronic)Maintenance to i(ntelligent)Maintenance2012In: The Ninth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, 2012, Vol. 1, p. 203-216Conference paper (Refereed)
    Abstract [en]

    iMaintenance stands for integrated, intelligent and immediate maintenance. It integrates various maintenance functions and connects these to all devices, using advanced communication technologies. The main challenge is to integrate the disparate systems and capabilities developed under current eMaintenance models and to make them immediately accessible through intelligent computing technologies. iMaintenance systems are computer-based, able to evolve with the system that they monitor and control, and they can be embedded in the system’s components, providing the ability to integrate new functionality with no downtime. This article will show how iMaintenance systems can provide decision-making support, thereby going beyond merely connecting various maintenance systems.

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  • 78.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Wandt, Karina
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Karim, Ramin
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    Department of Design Engineering and Manufacturing, University of Zaragoza.
    The evolution from e(lectronic)Maintenance to i(ntelligent)Maintenance2012In: Insight: Non-Destructive Testing & Condition Monitoring, ISSN 1354-2575, E-ISSN 1754-4904, Vol. 54, no 8, p. 446-450Article in journal (Refereed)
    Abstract [en]

    iMaintenance stands for integrated, intelligent and immediate maintenance. It integrates various maintenance functions and connects these to all devices using advanced communication technologies. The main challenge is to integrate the disparate systems and capabilities developed under current eMaintenance models and to make them immediately accessible through intelligent computing technologies. iMaintenance systems are computer-based, able to evolve with the system that they monitor and control and they can be embedded in the system's components, providing the ability to integrate new functionality with no downtime. This article will show how iMaintenance systems can provide decision-making support, thereby going beyond merely connecting various maintenance systems.

  • 79.
    Galar Pascual, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    University of Zaragoza, Spain.
    Lambán, Pilar
    University of Zaragoza, Spain.
    Tormos, Bernardo
    Polytechnic University of Valencia, Spain.
    The measurement of maintenance function efficiency through financial KPIS: [La medición de la eficiencia de la función mantenimiento a través de KPIs financieros]2014In: Dyna, ISSN 0012-7353, E-ISSN 2346-2183, 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.

  • 80.
    Galar Pascual, 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.
    Maintenance Audits Handbook: A Performance Measurement Framework2016Book (Other academic)
  • 81.
    Galvez, Antonio
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain.
    Diez-Olivan, Alberto
    TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain.
    Seneviratne, Dammika
    TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Basque Research and Technology Alliance (BRTA), 48170 Derio, Spain.
    Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach2021In: Sustainability, E-ISSN 2071-1050, Vol. 13, no 12, article id 6828Article in journal (Refereed)
    Abstract [en]

    Heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of degradation. This paper proposes a hybrid model-based approach (HyMA) to overcome the lack of failure data on a HVAC system installed in a passenger train carriage. The proposed HyMA combines physics-based models with data-driven models to deploy diagnostic and prognostic processes for a complex and critical system. The physics-based model generates data on healthy and faulty working conditions; the faults are generated in different levels of degradation and can appear individually or together. A fusion of synthetic data and measured data is used to train, validate, and test the proposed hybrid model (HyM) for fault detection and diagnostics (FDD) of the HVAC system. The model obtains an accuracy of 92.60%. In addition, the physics-based model generates run-to-failure data for the HVAC air filter to develop a remaining useful life (RUL) prediction model, the RUL estimations performed obtained an accuracy in the range of 95.21–97.80% Both models obtain a remarkable accuracy. The development presented will result in a tool which provides relevant information on the health state of the HVAC system, extends its useful life, reduces its life cycle cost, and improves its reliability and availability; thus enhancing the sustainability of the system.

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  • 82.
    Galvez, Antonio
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Tecnalia, Basque Research and Technology Alliance (BRTA), Derio, Spain.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Tecnalia, Basque Research and Technology Alliance (BRTA), Derio, Spain.
    Seneviratne, Dammika
    Tecnalia, Basque Research and Technology Alliance (BRTA), Derio, Spain.
    A Hybrid Model-Based Approach on Prognostics for Railway HVAC2022In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 108117-108127Article in journal (Refereed)
    Abstract [en]

    Prognostics and health management (PHM) of systems usually depends on appropriate prior knowledge and sufficient condition monitoring (CM) data on critical components’ degradation process to appropriately estimate the remaining useful life (RUL). A failure of complex or critical systems such as heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage may adversely affect people or the environment. Critical systems must meet restrictive regulations and standards, and this usually results in an early replacement of components. Therefore, the CM datasets lack data on advanced stages of degradation, and this has a significant impact on developing robust diagnostics and prognostics processes; therefore, it is difficult to find PHM implemented in HVAC systems. This paper proposes a methodology for implementing a hybrid model-based approach (HyMA) to overcome the limited representativeness of the training dataset for developing a prognostic model. The proposed methodology is evaluated building an HyMA which fuses information from a physics-based model with a deep learning algorithm to implement a prognostics process for a complex and critical system. The physics-based model of the HVAC system is used to generate run-to-failure data. This model is built and validated using information and data on the real asset; the failures are modelled according to expert knowledge and an experimental test to evaluate the behaviour of the HVAC system while working, with the air filter at different levels of degradation. In addition to using the sensors located in the real system, we model virtual sensors to observe parameters related to system components’ health. The run-to-failure datasets generated are normalized and directly used as inputs to a deep convolutional neural network (CNN) for RUL estimation. The effectiveness of the proposed methodology and approach is evaluated on datasets containing the air filter’s run-to-failure data. The experimental results show remarkable accuracy in the RUL estimation, thereby suggesting the proposed HyMA and methodology offer a promising approach for PHM.

  • 83.
    Galvez, Antonio
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, Vizcaya, 48170, Spain .
    Seneviratne, Dammika
    TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, Vizcaya, 48170, Spain .
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, Vizcaya, 48170, Spain .
    Development and synchronisation of a physics-based model for heating, ventilation and air conditioning system integrated into a hybrid model2021In: International Journal of Hydromechatronics, ISSN 2515-0464, Vol. 4, no 3Article in journal (Refereed)
    Abstract [en]

    This paper proposes a physics-based model which is part of a hybrid model (HyM). The physics-based model is developed for a heating, ventilation, and air conditioning (HVAC) system installed in a passenger train carriage. This model will be used to generate data for building a data-driven mode. Thus, the combination of these two models provides the hybrid model-based approach (HyMAs). The physics-based model of the HVAC system is divided into four principal parts: cooling subsystems, heating subsystems, ventilation subsystems, and vehicle thermal networking. First, the subsystems are modelled, considering the sensors embedded in the real system. Next, the model is synchronised with the real system to give better simulation results and validate the model. The cooling subsystem, heating subsystem and ventilation subsystem are validated with the acceptable sum square error (SSE) results. Second, the new virtual sensors are defined in the model, and their value to future research is suggested

  • 84.
    Gandhi, Kanika
    et al.
    Bharatiya Vidya Bhavan’s Usha & Lakshmi Mittal Institute of Mangement, Copernicus Lane, K. G. Marg, New Delhi.
    Jha, P.C
    Department of Operational Research, Faculty of Mathematical Sciences, University of Delhi.
    Govindan, Kannan
    Department of Business and Economics, University of Southern Denmark, Odense.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Three Echelon Supply Chain Design with Supplier Evaluation2014In: Proceedings of the Third International Conference on Soft Computing for Problem Solving: SocProS 2013 / [ed] Millie Pant; Kusum Deep; Atulya Nagar; Jagdish Chand Bansal, Berlin: Springer-Verlag GmbH , 2014, Vol. 2, p. 867-881Conference paper (Refereed)
    Abstract [en]

    Effective supply chain management (SCM), which facilitates companies to react to changing demand by swiftly communicating those needs to their supplier, is at the root of successful manufacturing. Optimizing a supply chain (SC) performance is a key factor for success in long term SC relationships. Much information like price, delivery time percentage and acceptance percentage are discussed in the process. A factor as imprecise demand is added in the same process that fuzzifies coordination between buyer and supplier. The paper considers nondeterministic conditions in the environment of business, coordination in procurement and distribution in a supplier selection problem that was proposed and a fuzzy model with two objectives was defined. The proposed model is a “fuzzy bi-objective mixed integer nonlinear” problem. A “fuzzy solution and fuzzy goal programming method” is used to convert the model into crisp form and solved using differential evolution

  • 85.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Fuzzy condition monitoring of recirculation fans and filters2016In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 7, no 4, p. 469-479Article in journal (Refereed)
    Abstract [en]

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

  • 86.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Scholz, Dieter
    Hamburg University of Applied Sciences, Aero - Aircraft Design and Systems Group.
    Automated parameter optimization for feature extraction for condition monitoring2016In: 14th IMEKO TC10 Workshop on Technical Diagnostics 2016: New Perspectives in Measurements, Tools and Techniques for Systems Reliability, Maintainability and Safety, Milan, Italy, 27-28 June 2016, 2016, p. 452-457Conference paper (Refereed)
    Abstract [en]

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

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  • 87.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Scholz, Dieter
    Hamburg University of Applied Sciences, Aero - Aircraft Design and Systems Group.
    Decision trees and the effects of feature extraction parameters for robust sensor network design2017In: Eksploatacja i Niezawodność – Maintenance and Reliability, ISSN 1507-2711, E-ISSN 2956-3860, Vol. 19, no 1, p. 31-42Article in journal (Refereed)
    Abstract [en]

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

  • 88.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Scholz, Dieter
    Hamburg University of Applied Sciences, Aero - Aircraft Design and Systems Group.
    Genetic algorithms and decision trees for condition monitoring and prognosis of A320 aircraft air conditioning2017In: Insight: Non-Destructive Testing & Condition Monitoring, ISSN 1354-2575, E-ISSN 1754-4904, Vol. 59, no 8, p. 424-433Article in journal (Refereed)
    Abstract [en]

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

  • 89.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Scholz, Dieter
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Effects of condition-based maintenance on costs caused by unscheduled maintenance of aircraft2016In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 22, no 4, p. 394-417Article in journal (Refereed)
    Abstract [en]

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

  • 90.
    Ghodrati, Behzad
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ahmadi, Alireza
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Reliability Analysis of Switches and Crossings: A Case Study in Swedish Railway2017In: International Journal of Railway Research, ISSN 2423-3838, Vol. 4, no 1, p. 1-12Article in journal (Refereed)
    Abstract [en]

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

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

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

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  • 91.
    Ghodrati, Behzad
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ahmadi, Alireza
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Reliability analysis of switches and crossings: a case study in Swedish railway2013Conference paper (Refereed)
    Abstract [en]

    It is reported that switches and crossings (S&C) are one of the subsystems that cause the most delays on Swedish Railways while accounting for at least 13% of maintenance costs [6]. It is the main reason why we chose to base our study on this subsystem.Intelligent data processing allows understanding the real reliability characteristics of the assets to be maintained. The first objective of this research is to determine the S&C reliability characteristics based on field data collection. Because field failure data are typically strongly censored, an especial statistics software package was developed to process field failure data, as commercial packages have not been found satisfactory in that respect. The resulting software, named RDAT® (Reliability Data Analysis Tool) has been relied upon for this study: it is especially adapted to statistical failure data analysis.In the next step the availability of studied switches and crossings is estimated based on the reliability characteristics founded in the first step.

    Download full text (pdf)
    fulltext
  • 92.
    Ghodrati, Behzad
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ahmadi, Alireza
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Spare parts estimation for machine availability improvement addressing its reliability and operating environment: case study2013In: International Journal of Reliability, Quality and Safety Engineering (IJRQSE), ISSN 0218-5393, Vol. 20, no 3Article in journal (Refereed)
    Abstract [en]

    Industrial operation cost analysis shows that, in general, maintenance represents a significant proportion of the overall operating costs. Therefore, the improvement of maintenance follows the final goal of any company, namely, to maximize profit. This paper studies spare parts availability, an issue of the maintenance process, which is an important way to improve production through increased availability of functional machinery and subsequent minimization of the total production cost. Spare parts estimation based on machine reliability characteristics and operating environment is performed. The study uses an improved statistical-reliability (S-R) approach which incorporates the system/machine operating environment information in systems reliability analysis. For this purpose, two methods of Poisson process and renewal process are introduced and discussed. The renewal process model uses a multiple regression type of analysis based on Cox’s proportional hazards modeling (PHM). The parametric approaches with baseline Weibull hazard functions and time independent covariates are considered, and the influence of operating environment factors on this model is analyzed. The outputs represent a significant difference in the required spare parts estimation when considering or ignoring the influence of the relevant system operating environment. The difference is significant in the sense of spare parts forecasting and inventory management which can enhance the parts and consequently machine availability, leading to economical operation and savings.

  • 93.
    González-González, Asier
    et al.
    Tecnalia Research and Innovation, Industry and Transport Division, Miñano.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Condition monitoring of wind turbine pitch controller: A maintenance approach2017In: 15th IMEKO TC10 Workshop on Technical Diagnostics 2017: "Technical Diagnostics in Cyber-Physical Era", 2017, p. 200-206Conference paper (Refereed)
    Abstract [en]

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

  • 94.
    González-González, Asier
    et al.
    Tecnalia Research and Innovation, Industry and Transport Division, Miñano.
    Jimenez Cortadi, Alberto
    Tecnalia Research and Innovation, Industry and Transport Division, Miñano.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Tecnalia Research and Innovation, Industry and Transport Division, Miñano .
    Ciani, Lorenzo
    University of Florence, Department of Information Engineering.
    Condition Monitoring of Wind Turbine Pitch Controller: A Maintenance Approach2018In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 123, p. 80-93Article in journal (Refereed)
    Abstract [en]

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

  • 95.
    Gustafson, Anna
    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.
    Schunnesson, Håkan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Heyns, Stephan
    Division of Structural Mechanics, Pretoria.
    Fusion of production, operation and maintenance data for underground mobile mining equipment2012In: The Ninth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, 2012, Vol. 2, p. 783-791Conference paper (Refereed)
    Abstract [en]

    For integration purposes, a data collection and distribution system based on the concept of cloud computing could be possible to use for collection of data or information pertaining from various sources of data. From a maintenance point of view, the benefit of cloud computing is that information or data may be collected on the health, variability, performance or utilization of the asset. It is especially useful in data mining where different types of data of different quality must be integrated. This paper discusses the concept and presents one example from the underground mining industry.

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  • 96.
    Gustafson, Anna
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Lipsett, Michael
    Mechanical Engineering, University of Alberta, Edmonton.
    Schunnesson, Håkan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    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.
    Development of a Markov model for production performance optimisation: Application for semi-automatic and manual LHD machines in underground mines2014In: International Journal of Mining, Reclamation and Environment, ISSN 1748-0930, E-ISSN 1748-0949, Vol. 28, no 5, p. 342-355Article in journal (Refereed)
    Abstract [en]

    This paper compares three ways to operate a load haul dump (LHD) machine, manual operation, automatic operation (fleet operation) and semi-automatic operation, to find the best operating mode. In a fault tree analysis, different failures are classified and analysed, but the way to recover from certain states is not accounted for, which is something a Markov model can handle. The paper is based on the analysis of real data from an underground mine. A Markov model has been built for mining application and it is shown that a semi-automatic LHD has the highest probability of being in a productive state since it has the advantage of changing operating modes (manual and automatic) depending on the need and situation. Hence, the semi-automatic LHD is the best choice from an operational point of view. The paper fills a gap in the literature on manual vs. automatically operated LHDs by providing a new way of evaluating the operating mode of LHDs using Markov modelling, while considering the operating environment.

  • 97.
    Gustafson, Anna
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Schunnesson, Håkan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Maintenance indicators for underground mining equipment: a case study of automatically versus manually operated LHD machines2011In: 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, Stavanger: COMADEM International, 2011, p. 1205-1214Conference paper (Refereed)
    Abstract [en]

    Key Performance Indicators (KPIs) are performance measures directly related to the overall goals of the company and some of them depend on the maintenance function. In mining companies top managers use the maintenance cost per unit versus budget as one of the KPIs. However many other technical, organizational and economical parameters in a company can be helpful during the decision making process.In this paper the productivity of Load-Haul-Dump machines (LHDs), that is obtained when manual and/or automatic mode are used, are being analysed. The correlation between the productivity and the maintenance KPIs as well as the issues related to the acquisition of data will be shown in this paper highlighting the complexity of getting accurate decision process parameters. It is recognized that the data for some of the components and failure modes originating from different sources are not compatible. This situation must be considered when compiling the data, especially to permit comparison the data should be made compatible. The problem of incompatibility is most severe when dealing with demand related failures. The philosophy and mechanisms of demand related failures as well as the methods used to denote the time and demand related failures in common form have to be taken into account.

  • 98.
    Gustafson, Anna
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Schunnesson, Håkan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    TPM framework for underground mobile mining equipment: a case study2011Conference paper (Refereed)
    Abstract [en]

    In underground mines, mobile mining equipment is critical to the production system. Drill rigs for development and production, vehicles for charging holes, LHDs for loading and transportation, scaling rigs and rigs for reinforcement and cable bolting are all important units in the process to generate a continuous ore flow. For today’s mining companies, high equipment availability is essential to reduce operational and capital costs and to maintain high production. High and controllable reliability is also important especially in attempts to automate the production equipment. This paper compares existing maintenance work in a Swedish and a Tanzanian mine. The various maintenance procedures are identified and evaluated based on a TPM framework.

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  • 99.
    Gustafson, Anna
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Schunnesson, Håkan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering.
    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.
    Production and maintenance performance analysis: manual versus semi-automatic LHDs2013In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 19, no 1, p. 74-88Article in journal (Refereed)
    Abstract [en]

    Purpose – The purpose of this paper is to evaluate and analyse the production and maintenance performance of a manual and a semi-automatic Load Haul Dump (LHD machine to find similarities and differences.Design/methodology/approach – Real time process-, operational- and maintenance data, from an underground mine in Sweden, have been refined and aggregated into KPIs in order to make the comparison between the LHDs.Findings – The main finding is the demonstration of how production and maintenance data can be improved through information fusion, showing some unexpected result for maintenance of automatic and semi-automatic LHDs in the mining industry. It was found that up to one third of the manually entered workshop data are not consistent with the automatically recorded production times. It is found that there are similarities in utilization and filling rate but differences in produced tonnes/machine hour between the two machines.Originality/value – The originality in this paper is the information fusion between automatically produced production data and maintenance data which increases the accuracy of reliability analysis data. Combining the production indicator and the maintenance indicator gives a common tool to the production and maintenance departments. This paper shows the difference in both maintenance and production performance between a manual and semi-automatic LHD.

  • 100.
    Gustafson, Anna
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Schunnesson, Håkan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    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.
    The influence of the operating environment on manual and automated load-haul-dump machines: a fault tree analysis2013In: International Journal of Mining, Reclamation and Environment, ISSN 1748-0930, E-ISSN 1748-0949, Vol. 27, no 2, p. 75-87Article in journal (Refereed)
    Abstract [en]

    The automated load-haul-dump (LHD) machines have the potential to increaseproductivity and improve safety, but there are many issues to be considered when optimising the operation of LHDs. Today’s focus on improved equipment reliability is part of the problem, and another difficult issue is the special conditions and constraints of the operating environment. For automated LHDs, the latter issue is even more important, as humans have been removed from the production area and are not close by to solve the problems. The purpose of this paper is to find the causes of LHD idle time and to study their impact on the operation of LHDs. In this study, real-time process data and maintenance data from an underground mine in Sweden have been refined and integrated. The study takes into account the complexity of the mine environment, discusses the factors to be considered when optimising and automating the operation and uses fault tree analysis (FTA) to analyse the idle time.

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