Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. Nowadays anomaly detection method deployed to production is based on reconstruction error generated by LSTM sequence modeling. Recently, the remarkable improvement achieved by BERT model in language translation demonstrated that the self-attention-based transformer is superior to LSTM models, due to its ignoring distance. In this paper, we continue the research on transformer and propose a transformer-based architecture, masked time series modeling, applying transformer in data stream, which has two novel components (1) the attention mechanism used for updating timestep in parallel and (2) the mask strategy used to detect the anomaly in advanced time. We compared the performances of our method with state-of-the-art AD methods on challenging public NASA telemetry dataset. The experiment results demonstrated our method saves about 80% time cost because of parallel computing compared with LSTM methods and achieves 0.78 F1 point-based score, moreover achieving a better score on range-based indicators.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Spacecraft Anomaly Detection via Transformer Reconstruction Error


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Jing, Zhongliang (Herausgeber:in) / Meng, Hengyu (Autor:in) / Zhang, Yuxuan (Autor:in) / Li, Yuanxiang (Autor:in) / Zhao, Honghua (Autor:in)

    Kongress:

    International Conference on Aerospace System Science and Engineering ; 2019 ; Toronto, ON, Canada July 30, 2019 - August 01, 2019



    Erscheinungsdatum :

    2020-03-01


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Spacecraft Anomaly Detection via Transformer Reconstruction Error

    Meng, Hengyu / Zhang, Yuxuan / Li, Yuanxiang et al. | British Library Conference Proceedings | 2020


    Spacecraft Anomaly Detection via Transformer Reconstruction Error

    Meng, Hengyu / Zhang, Yuxuan / Li, Yuanxiang et al. | TIBKAT | 2020


    Dawn Spacecraft Test Anomaly

    Tucker, Shonte | NTRS | 2021


    NOAA's spacecraft anomaly data base

    WILKINSON, DANIEL | AIAA | 1990


    Improving Spacecraft Health Monitoring with Automatic Anomaly Detection Techniques

    Fuertes, Sylvain / Picart, Gilles / Tourneret, Jean-Yves et al. | AIAA | 2016