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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Spacecraft Anomaly Detection via Transformer Reconstruction Error


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:

    Conference:

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



    Publication date :

    2020-03-01


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    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


    SADDLE: Spacecraft Anomaly Detection using Deep Learning

    Srivastava, Ankit / Badal, Neeraj / Manoj, BS | IEEE | 2024


    Dawn Spacecraft Test Anomaly

    Tucker, Shonte | NTRS | 2021


    Semi-Supervised Machine Learning for Spacecraft Anomaly Detection & Diagnosis

    Ramachandran, Sowmya / Rosengarten, Maia / Belardi, Christian | IEEE | 2020