Abstract Telemetry anomaly detection is a prominent health condition monitoring task that plays an increasingly crucial role in discovering potential incidents and facilitating long-term reliable on-orbit operations of satellite. Nevertheless, multiple monitored telemetry parameters and the complicated correlations among them pose significant challenges. In this study, an encoder-decoder generative adversarial network (EDGAN)-based anomaly detector is proposed for detecting multivariate anomalies in telemetry data. Firstly, an additional encoder is merged into the generator to learn a latent representation of the multivariate telemetry data for generating a reasonable and enhanced substitute of the data. Secondly, a feature matching strategy is applied in the discriminator to facilitate extracting more distinguishable features. Thirdly, three loss functions are designed for model training, and the deviation from the data space of the generator is utilized as the criterion to detect anomalies. Finally, experiments on real-world and public satellite datasets are conducted and the results verify the effectiveness of the proposed method.

    Highlights A new generative framework EDGAN is proposed for unsupervised anomaly detection of multivariate telemetry data. Multiple loss functions are designed for adversarial training of EDGAN. Multivariate and contextual anomalies are able to be detected. Experiments on real-world and public multivariate telemetry datasets verify the effectiveness of EDGAN.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An encoder-decoder generative adversarial network-based anomaly detection approach for satellite telemetry data


    Contributors:
    Xu, Zhaoping (author) / Cheng, Zhijun (author) / Tang, QiDeng (author) / Guo, Bo (author)

    Published in:

    Acta Astronautica ; 213 ; 547-558


    Publication date :

    2023-09-20


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Aircraft brake system anomaly detection method based on generative adversarial network and auto-encoder

    ZHANG DONGPING / WANG MENGTING / XIA YUEJIAN et al. | European Patent Office | 2023

    Free access

    EVALUATING ANOMALY DETECTION IN SATELLITE TELEMETRY DATA

    Nalepa, Jakub / Benecki, Pawel / Andrzejewski, Jacek et al. | TIBKAT | 2022


    Anomaly Detection Using Convolutional Neural Network and Generative Adversarial Network

    Mohanan, Amritha / Padathil Veerendrakumar, Praveen / Padmanabha Rajeswari, Priyanka Pillai et al. | SAE Technical Papers | 2023



    Anomaly Monitoring Framework in Lane Detection With a Generative Adversarial Network

    Kim, Hayoung / Park, Jongwon / Min, Kyushik et al. | IEEE | 2021