Tandem Connectionist Anomaly Detection: Use of Faulty Vibration Signals in Feature Representation Learning

Takanori Hasegawa, Jun Ogata, Masahiro Murakawa, Tetsuji Ogawa

    研究成果: Conference contribution

    2 引用 (Scopus)

    抜粋

    An effective use of faulty-state data is proposed to achieve robust, accurate data-driven anomaly (fault) detection for rotating machine. Although using faulty data in the training process generally can improve the performance of anomaly detection system, it is rare to obtain enough samples to train failures or defects on a target machine. We therefore utilize the existing data from non-target (different-type) machines for feature representation learning to improve anomaly detection for the target machine. Specifically, deep neural networks (DNNs) that are trained to discriminate the normal and faulty states of the non-target machines are used to extract features. The extracted features are then taken as inputs to an anomaly detector based on Gaussian mixture models (GMMs). This architecture is called DNN/GMM tandem connectionist anomaly detection. Experimental comparisons using vibration signals from actual wind turbine components demonstrated that the developed tandem connectionist system yielded significant improvements over existing systems, and that the representation learning performed robustly with respect to differences in machine types.

    元の言語English
    ホスト出版物のタイトル2018 IEEE International Conference on Prognostics and Health Management, ICPHM 2018
    出版者Institute of Electrical and Electronics Engineers Inc.
    ISBN(電子版)9781538611647
    DOI
    出版物ステータスPublished - 2018 8 27
    イベント2018 IEEE International Conference on Prognostics and Health Management, ICPHM 2018 - Seattle, United States
    継続期間: 2018 6 112018 6 13

    出版物シリーズ

    名前2018 IEEE International Conference on Prognostics and Health Management, ICPHM 2018

    Conference

    Conference2018 IEEE International Conference on Prognostics and Health Management, ICPHM 2018
    United States
    Seattle
    期間18/6/1118/6/13

    ASJC Scopus subject areas

    • Statistics, Probability and Uncertainty
    • Civil and Structural Engineering
    • Safety, Risk, Reliability and Quality

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  • これを引用

    Hasegawa, T., Ogata, J., Murakawa, M., & Ogawa, T. (2018). Tandem Connectionist Anomaly Detection: Use of Faulty Vibration Signals in Feature Representation Learning. : 2018 IEEE International Conference on Prognostics and Health Management, ICPHM 2018 [8448450] (2018 IEEE International Conference on Prognostics and Health Management, ICPHM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPHM.2018.8448450