Automotive fault diagnosis based on SVM and particle swarm algorithm
基于粒子群算法优化支持向量机汽车故障诊断研究

Automobile fault detection and diagnosis technology has been a research hotspot. Support vector machine used in automobile fault diagnosis, the classification decision on the rate of correct classification and diagnosis time have great influence. In order to effectively improve the automobile fault diagnosis efficiency and accuracy, this paper proposed a method based on particle swarm optimization algorithm for hierarchical support vector machine fault diagnosis detection method. According to the decomposition support vector machine has short test time, is difficult to confirm the structure characteristics, this paper used the particle swarm algorithm, based on the maximum distance principle optimization of hierarchical support vector machine model, so that each node of the support vector machine had the maximal margin classification, reduced the error accumulation, thus optimized the multilevel binary tree structure of SVM, to realize fault hierarchical diagnosis. The simulation results show that the proposed algorithm, all the reference model of the highest accuracy, can be efficient for automobile system fault detection and location. This algorithm has strong generalization ability, and can shorten the time of fault diagnosis.