Abstract Accurate fault detection and isolation in machines requires image processing of measurement signals which are contaminated with noise. Typically, faults are revealed by sharp trend shifts in the signals and these trend shifts should be preserved during image processing. Linear filters can smooth out the sharp trend shifts while removing noise. However, nonlinear filters such as the weighted recursive median (WRM) filters show good noise reduction while preserving key image features if their integer weights are determined optimally. The ant colony optimization (ACO) method coupled with local search to calculate the integer weights of WRM filters. It is found that the filter weight optimization problem is mathematically equivalent to the quadratic assignment problem which can be solved by ACO. Optimal parameters for the ACO are found using numerical experiments. The WRM filter is demonstrated for abrupt and gradual faults in gas turbine machine and is found to yield noise reduction of 52–64% for simulated noisy data considered in this chapter.


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    Titel :

    Image Processing Filters for Machine Fault Detection and Isolation


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    2019-10-01


    Format / Umfang :

    17 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch







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