To improve the reliability of autonomous vehicles, open-set fault diagnosis is indispensable to jointly detect known and unknown faults, in which unknown faults only appear in the testing set. However, in learning the representations for open-set diagnosis, the extracted representations lack hierarchy to preserve high-level and genuine representations, and the final representations utilized for diagnosing lack distinctiveness to separate unknowns from knowns. In addition, in the stage of testing, the open-set diagnosis models are error-prone when unknowns are similar to knowns. Motivated by these challenges, we propose a Multi-hop Attentive Graph Variational Autoencoder (MAGVA) model for open-set fault diagnosis in this paper. First, a multi-hop attentive graph convolutional network is developed to adaptively extract hierarchical representations and eliminate unknown fault misidentification. Then, to avoid unknown faults occupying the same region as known faults and identify known faults, structural representation constraints are designed by jointly conducting reconstruction with an intra-class constraint and classification with an inter-class constraint. Finally, combining the distinguishable representations learned by MAGVA, a generative distance-based open-set diagnosis algorithm is proposed, in which the procedures of estimating class-conditional distributions are designed, and a relative generative distance is then presented to derive diagnosis results under the class-conditional distributions. Experiments on three commonly used bearing datasets for vehicles demonstrate that the proposed MAGVA consistently outperforms the compared models in open-set, closed-set, and unknown fault diagnosis.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    MAGVA: An Open-Set Fault Diagnosis Model Based on Multi-Hop Attentive Graph Variational Autoencoder for Autonomous Vehicles


    Contributors:
    Fu, Rao (author) / Bi, Yuanguo (author) / Han, Guangjie (author) / Zhang, Xiaoling (author) / Liu, Li (author) / Zhao, Liang (author) / Hu, Bing (author)

    Published in:

    Publication date :

    2023-12-01


    Size :

    2757314 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Sensor Fault Detection and Diagnosis for autonomous vehicles

    Realpe Miguel / Vintimilla Boris / Vlacic Ljubo | DOAJ | 2015

    Free access



    Crash data augmentation using variational autoencoder

    Islam, Zubayer / Abdel-Aty, Mohamed / Cai, Qing et al. | Elsevier | 2020


    FedVAE: Trajectory privacy preserving based on Federated Variational AutoEncoder

    Jiang, Yuchen / Wu, Ying / Zhang, Shiyao et al. | IEEE | 2023