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T09. Industrial time series, anomaly detection, and predictive maintenance

Sensor-driven fault detection, remaining useful life, and condition monitoring research.

  1. T09-6Structural parallel

    Anomaly Detection in Time Series: A Comprehensive Evaluation

    Authors
    Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock
    Year
    2022
    Venue
    Proceedings of the VLDB Endowment vol. 15 no. 9 pp. 1779 pp. 1797

    Korean review of T09-6

  2. T09-8Structural parallel

    Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network (OmniAnomaly)

    Authors
    Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, Dan Pei
    Year
    2019
    Venue
    KDD 2019

    Korean review of T09-8

  3. T09-10Structural parallel

    Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

    Authors
    Jiehui Xu, Haixu Wu, Jianmin Wang, Mingsheng Long
    Year
    2022
    Venue
    ICLR 2022 (Spotlight)

    Korean review of T09-10

  4. T09-11Structural parallel

    TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

    Authors
    Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings
    Year
    2022
    Venue
    Proceedings of the VLDB Endowment vol. 15 no. 6 pp. 1201 pp. 1214

    Korean review of T09-11

  5. T09-12Structural parallel

    Time-Series Anomaly Detection Service at Microsoft

    Authors
    Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, Qi Zhang
    Year
    2019
    Venue
    KDD 2019

    Korean review of T09-12

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