【摘要】 针对汽车涂装线设备故障无法及时发现和排除的困难,提出基于支持向量机的汽车涂装线设备故障诊断方法.该方法采用统计学习理论中结构风险最小化原则,克服了传统渐进理论机器学习算法的不足,适用于有限样本下模式识别问题,使预测结果更准确.依据烘房加热系统监测参数和故障类型构建SVM分类器,并采用交叉验证网格搜索法寻优各分类器的核参数及惩罚参数,建立SVM故障诊断模型;将PCA降维后的样本参数进行充分仿真训练,仿真结果表明该方法能够有效地对设备故障进行分类.更多还原

【Abstract】 Aiming at the difficulty in discovering and eliminating the system faults of automobile coating equipment promptly,a new method of fault diagnosis based on SVM was proposed.The method was created by the structural risk minimization principle in the statistical learning theory,which overcame the weakness of the traditional learning method in asymptotic theory.And it was suitably used in pattern recognition with finite samples and made the output more precise.The classifiers were also structured according to the equipment monitoring parameters and fault types of the heating system.The optimal kernel parameters and punished parameters were searched by the cross validation grid search method in order to establish the SVM model for fault diagnosis.The samples of dimension reducing by the PCA were then taken into training in the model.It was finally reveals by the simulation experiment that it can successfully and precisely accomplish the fault diagnosis with the SVM method.更多还原