基于K均值和支持向量机的燃料电池在线自适应故障诊断

A K-Means/Support Vector Machine Based SelfAdaptive Online Fault Diagnosis Method for Fuel Cell Systems

摘要:

基于K均值（Kmeans）和支持向量机（support vector machine, SVM）算法，提出了一种车用燃料电池系统（fuel cell system, FCS）在线自适应故障诊断方法.该方法通过不断获取系统最新单体电压，采用Kmeans算法改进传统的静态SVM分类器模型，对实时获取的信息进行聚类，实现分类器的在线自适应调节.采用已发表文献中的实验数据进行了相关的验证分析，结果表明，提出的方法能有效地在线调节故障分类器，实现FCS系统特性发生改变后的故障检测.

Abstract:

Based on k-means and support vector machine (SVM) algorithms, an online self-adaptive fault diagnosis method for automotive fuel cell system (FCS) is proposed. By continuously acquiring cell voltages and using k-means clustering to improve the original SVM classifier model, this method can achieve online self-adaption of the classifier. The experimental data from published papers were used to verify and analyze the results. The results show that the proposed method can effectively adjust the fault classifier online to detect the fault after changing the FCS system characteristics.