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  • 1.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Gustafson, Anna
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Tormos, Bernardo
    CMT - Motores Termicos, Uiversitat Politècnica de València, Valencia, Spain.
    Berges, Luis
    Department Design Engineering and Manufacturing, University of Zaragoza, Zaragoza, Spain.
    Maintenance Decision Making based on different types of data fusion: [Podejmowanie decyzji eksploatacyjnych w oparciu o fuzję różnego typu danych]2012In: Eksploatacja i Niezawodność – Maintenance and Reliability, ISSN 1507-2711, E-ISSN 2956-3860, Vol. 14, no 2, p. 135-144Article in journal (Refereed)
    Abstract [en]

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

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  • 2.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    Department Design engineering and manufacturing, University of Zaragoza.
    Sandborn, Peter
    CAlCe Center for Advanced life Cycle engineering, University of Maryland.
    The need for aggregated indicators in performance asset management2014In: Eksploatacja i Niezawodność – Maintenance and Reliability, ISSN 1507-2711, E-ISSN 2956-3860, Vol. 16, no 1, p. 120-127Article in journal (Refereed)
    Abstract [en]

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

  • 3.
    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

  • 4.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Multi-body modelling of rolling element bearings and performance evaluation with localised damage2016In: Eksploatacja i Niezawodność – Maintenance and Reliability, ISSN 1507-2711, E-ISSN 2956-3860, Vol. 18, no 4, p. 638-648Article in journal (Refereed)
    Abstract [en]

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

  • 5.
    Lin, Jing
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Asplund, Matthias
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Comparison study of heavy haul locomotive wheels' running surfaces wearing: [Badania PoróWnawcze Zużycia Powierzchni Bieżnych Kół Lokomotyw DużEj Mocy]2014In: Eksploatacja i Niezawodność – Maintenance and Reliability, ISSN 1507-2711, E-ISSN 2956-3860, Vol. 16, no 2, p. 276-287Article in journal (Refereed)
    Abstract [en]

    The service life of railway wheels can differ significantly depending on their installed position, operating conditions, re-profiling characteristics, etc. This paper compares the wheels on two selected locomotives on the Iron Ore Line in northern Sweden to explore some of these differences. It proposes integrating reliability assessment data with both degradation data and re-profiling performance data. The following conclusions are drawn. First, by considering an exponential degradation path and given operation condition, the Weibull frailty model can be used to undertake reliability studies; second, among re-profiling work orders, rolling contact fatigue (RCF) is the principal reason; and third, by analysing re-profiling parameters, both the wear rate and the re-profiling loss can be monitored and investigated, a finding which could be applied in optimisation of maintenance activities.

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  • 6.
    Raposo, Hugo
    et al.
    CEMMPRE - Centre for Mechanical Engineering, Materials and Processes, University of Coimbra.
    Farinha, José Torres
    CEMMPRE - Centre for Mechanical Engineering, Materials and Processes, University of Coimbra.
    Ferreira, Luis A.
    UP – University of Porto.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    An integrated econometric model for bus replacement and determination of reserve fleet size based on predictive maintenance2017In: Eksploatacja i Niezawodność – Maintenance and Reliability, ISSN 1507-2711, E-ISSN 2956-3860, Vol. 19, no 3, p. 358-368Article in journal (Refereed)
    Abstract [en]

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

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