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  • 1.
    Saari, Juhamatti
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Odelius, Johan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Luleå University of Technology, SKF-LTU University Technology Centre.
    Detecting operation regimes using unsupervised clustering with infected group labelling to improve machine diagnostics and prognostics2018In: Operations Research Perspectives, ISSN 2214-7160, Vol. 5, p. 232-244Article in journal (Refereed)
    Abstract [en]

    Estimating the stress level of components while operation modes are varying is a key issue for many prognostic models in condition monitoring. The identification of operation profiles during production is therefore important. Clustering condition monitoring data with regard to operation regimes will provide more detailed information about the variation of stress levels during production. The distribution of the operation regimes can then support prognostics by revealing the cause-and-effect relationship between the operation regimes and the wear level of components.

    In this study unsupervised clustering technique was used for detecting operation regimes for an underground LHD (load-haul-dump machine) by using features extracted from vibration signals measured on the front axle and the speed of the Cardan axle. The clusters were also infected with a small portion of the data to obtain the corresponding labels for each cluster. Promising results were obtained where each sought-for operation regime was detected in a sensible manner using vibration RMS values together with speed.

  • 2.
    Saari, Juhamatti
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Luleå University of Technology, SKF-LTU University Technology Centre.
    Strömbergsson, Daniel
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements. Luleå University of Technology, SKF-LTU University Technology Centre.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Thomson, A.
    SKF (U.K), Livingston, Scotland, United Kingdom.
    Detection and identification of windmill bearing faults using a one-class support vector machine (SVM)2019In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 137, p. 287-301Article in journal (Refereed)
    Abstract [en]

    The maintenance cost of wind turbines needs to be minimized in order to keep their competitiveness and, therefore, effective maintenance strategies are important. The remote location of wind farms has led to an opportunistic maintenance strategy where maintenance actions are postponed until they can be handled simultaneously, once the optimal opportunity has arrived. For this reason, early fault detection and identification are important, but should not lead to a situation where false alarms occur on a regular basis. The goal of the study presented in this paper was to detect and identify wind turbine bearing faults by using fault-specific features extracted from vibration signals. Automatic identification was achieved by training models by using these features as an input for a one-class support vector machine. Detection models with different sensitivity were trained in parallel by changing the model tuning parameters. Efforts were also made to find a procedure for selecting the model tuning parameters by first defining the criticality of the system and using it when estimating how accurate the detection model should be. Method was able to detect the fault earlier than using traditional methods without any false alarms. Optimal combination of features and model tuning parameters was not achieved, which could identify the fault location without using any additional techniques.

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