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Author: search.filters.author.Songsuwankit, KanoknuchSubject: search.filters.subject.MCSA

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    Comparison of Reduced-Length FFT-Based Feature for Induction Motor Fault Classification
    (2025-01-01)
    Taweewat, Pat
    ;
    Suwan-Ngam, Warachart
    ;
    Songsuwankit, Kanoknuch
    ;
    Konghuayrob, Poom
    This research presents a comparison of FFT-based features which can be used for classifying induction motor faults via neural network. In this paper, the misalignment and rotor bar damage faults are investigated by using stator current as input data only. As the length of the full FFT can include both informative data corresponding to the faults and uninformative data such as noise from environment or electrical supply, only relevant magnitude from FFT bins should be selected and used instead. This paper proposed to use threshold level determined from the magnitude of FFT bins in dataset as a criterion for the selection. From experimental results, an input feature vector created by proposed method can create short input feature vector length to be used by neural network efficiently. The trained neural network performs classification task at 99.98% in accuracy. Comparing to using dimension reduction by PCA, thresholding method needs basic computation, and yields result close to PCA method.
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    A Small Deep Learning Model for Fault Detection of a Broken Rotor Bar of an Induction Motor
    (2024-01-01)
    Taweewat, Pat
    ;
    Suwan-Ngam, Warachart
    ;
    Songsuwankit, Kanoknuch
    ;
    Konghuayrob, Poom
    In this paper, we present an investigation of a small deep learning model applied to the detection of a broken rotor bar of an induction motor. The motor current spectrum analysis is the base method for fault detection. This proposed method focuses on the analysis of the modification of the input vector and model configuration. This method was implemented and it showed that the feature length and size of the model are reduced compared with the existing method. The experimental results showed that only feature extraction using the spectral-based method and limit range of its coefficient are adequate to provide accuracy of small deep learning comparable to that of the parallel-layer deep learning model. Likewise, at the same accuracy level, based on the deep learning model, a shorter sampling duration than that required by the reference model is needed.