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

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 hierarc...

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Vydáno v:IEEE transactions on intelligent transportation systems Ročník 24; číslo 12; s. 14873 - 14889
Hlavní autoři: Fu, Rao, Bi, Yuanguo, Han, Guangjie, Zhang, Xiaoling, Liu, Li, Zhao, Liang, Hu, Bing
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation models
Algorithms
Artificial neural networks
Autonomous vehicles
Classification algorithms
Fault detection
Fault diagnosis
Faults
generative distance-based diagnosis algorithm
Mechatronics
multi-hop attentive graph variational autoencoder
Open-set fault diagnosis
Representations
structural representation constraints
Testing
Training
Vehicles
ISSN:1524-9050, 1558-0016
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3300911