Autonomous Transport System (ATS) architectures enable a wide range of new applications and bring significant benefits to transport systems. However, during the design stage, errors of the architecture can have an impact on the smooth implementation of the ATS, which will endanger the normal operation of the transport systems. To ensure a high autonomy of the ATS architecture, i.e., “functionally evolvable, logically reconfigurable and physically configurable”, the detection of ATS architecture design errors is essential. This paper aims to fill the research gap in the existing research on diagnosing or evaluating ATS architectures. Inspired by word embedding models in natural language processing communities, we propose a data-driven approach to diagnose ATS architectures without prior knowledge or rules. We use an architecture embedding model to generate vector representations of ATS architectures, then train the model through negative sampling of the training dataset to identify the features of abnormal ATS architecture. Finally, we employ the trained model to classify structural errors of the test dataset generated from the ATS architecture. The experimental results show that the proposed method gains a relatively good effect of classifying with an average accuracy of 79.3%, demonstrating the effectiveness of the method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Data-Driven Method for Diagnosing ATS Architecture by Anomaly Detection


    Additional title:

    Smart Innovation, Systems and Technologies


    Contributors:
    Bie, Yiming (editor) / Qu, Bob X. (editor) / Howlett, Robert J. (editor) / Jain, Lakhmi C. (editor) / Zhou, Aimin (author) / Cheng, Shaowu (author) / Li, Xiantong (author) / Li, Kui (author) / You, Linlin (author) / Cai, Ming (author)

    Conference:

    Proceedings of KES-STS International Symposium ; 2022 ; Rhodes, Greece June 20, 2022 - June 22, 2022



    Publication date :

    2022-05-15


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    A Data-Driven Method for Diagnosing ATS Architecture by Anomaly Detection

    Zhou, Aimin / Cheng, Shaowu / Li, Xiantong et al. | TIBKAT | 2022


    DRIVE CIRCUIT ANOMALY DIAGNOSING DEVICE

    KOJIMA HIROYUKI / AWANO KOICHIRO | European Patent Office | 2019

    Free access

    Drive circuit anomaly diagnosing device

    KOJIMA HIROYUKI / AWANO KOICHIRO | European Patent Office | 2021

    Free access

    DRIVE CIRCUIT ANOMALY DIAGNOSING DEVICE

    KOJIMA HIROYUKI / AWANO KOICHIRO | European Patent Office | 2020

    Free access