基于支持向量机的车用燃料电池系统故障诊断

为了提高车用燃料电池系统的安全可靠性和可维护性,考虑到其大量完整的故障样本难以获取,提出了一种基于二叉树多分类器的支持向量机故障诊断方法.首先,以自主研发的60 kW车用燃料电池系统为研究对象,分析了其故障机理和特征；然后,融合15种故障征兆参数并进行归一化预处理作为支持向量机的输入,以14种典型故障作为输出,选取径向基核函数并利用粒子群优化算法对支持向量机的惩罚参数和核函数参数进行优化,利用310组样本数据对其进行训练,通过90组测试样本测试实现了其典型故障的识别；最后,将支持向量机和神经网络分别在不同训练样本数下的故障诊断性能进行了对比.仿真结果表明,支持向量机具有较好的故障正判率和泛化能力,可有效用于车用燃料电池系统的多故障诊断.

Fault Diagnosis of Automotive Fuel Cell System Based on Support Vector Machine

In order to enhance the safety,reliability and maintainability of automotive fuel cell system,considering that the large quantities of complete faults sample data are difficult to obtain,a fault diagnosis method of support vector machine(SVM) based on binary tree multi-classifier is put forward.Firstly,based on the 60kW automotive fuel cell system designed by our group,the fault mechanism and characteristic are analyzed.Then,15 kinds of fused fault scenario are treated as the inputs of SVM after normalization,and 14 kinds of typical faults are taken as the outputs of SVM,in the training process,310 groups of sample data and the RBF kernel function are adopted,and the penalty parameter as well as kernal parameter of SVM are optimized with particle swarm optimization(PSO) algorithm,all the different typical faults can be identified from the experimental results with 90 groups of testing data.Finally,the fault diagnosis performance of SVM is compared with that of BP neural networks with different quantities of training sample data,the simulation results demonstrate that the proposed method has better capability of faults classification and generalization,and can be effectively implemented in the fault diagnosis of automotive fuel cell system.