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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Fault Diagnosis of an Autonomous Vehicle With an Improved SVM Algorithm Subject to Unbalanced Datasets
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Fault Diagnosis of an Autonomous Vehicle With an Improved SVM Algorithm Subject to Unbalanced Datasets

机译：具有不平衡数据集的改进SVM算法的自主车辆故障诊断

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摘要

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.
机译：安全是自动车辆的关键要求之一，故障诊断是提高车辆安全性的有效技术。基于模型的故障诊断方法将故障模拟到系统模型中，并通过观察者估算故障。在这篇文章中，避免了设计观察者的复杂性，我们研究转向制动器故障诊断的问题，基于基于模型的支持向量机（SVM）分类的办法自动车辆。系统模型用于生成残余信号作为训练数据，并且采用SVM分类的基于数据的算法来诊断故障。由于数据的现象不平衡引起的数据驱动方法，用线性判别分析的方法和使用被提议的修改和完善分类和性能故障优化灰狼的算法的阈值调整一个欠采样过程的性能差诊断。根据广泛使用的数据集进行各种比较。比较结果表明，所提出的算法对现有方法的分类具有优势。自动化车辆的实验结果和比较说明了所提出的算法对转向执行器故障诊断的有效性。

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