Fault Diagnosis of an Autonomous Vehicle With an Improved SVM Algorithm Subject to Unbalanced Datasets

Safety is one of the key requirements for automated vehicles and fault diagnosis is an effective technique to enhance the vehicle safety. The model-based fault diagnosis method models the fault into the system model and estimates the faults by observer. In this article, to avoid the complexity of de...

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Vydáno v:IEEE transactions on industrial electronics (1982) Ročník 68; číslo 7; s. 6248 - 6256
Hlavní autoři: Shi, Qian, Zhang, Hui
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Actuators
Algorithms
Automated driving vehicle
Automation
Autonomous vehicles
Classification
data unbalance
Datasets
Discriminant analysis
Fault diagnosis
Measurement
Observers
Performance enhancement
Steering
support vector machine (SVM)
Support vector machines
Vehicle safety
Vehicles
ISSN:0278-0046, 1557-9948
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Shrnutí:Safety is one of the key requirements for automated vehicles and fault diagnosis is an effective technique to enhance the vehicle safety. The model-based fault diagnosis method models the fault into the system model and estimates the faults by observer. In this article, to avoid the complexity of designing observer, we investigate the problem of steering actuator fault diagnosis for automated vehicles based on the approach of model-based support vector machine (SVM) classification. The system model is utilized to generate the residual signal as the training data and the data-based algorithm of the SVM classification is employed to diagnose the fault. Due to the phenomena of data unbalance induced poor performance of the data-driven method, an undersampling procedure with the approach of linear discriminant analysis and a threshold adjustment using the algorithm of grey wolf optimizer are proposed to modify and improve the performance of classification and fault diagnosis. Various comparisons are carried out based on widely used datasets. The comparison results show that the proposed algorithm has superiority on the classification over existing methods. Experimental results and comparisons of an automated vehicle illustrate the effectiveness of the proposed algorithm on the steering actuator fault diagnosis.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2020.2994868