SVM在煤矿机电设备故障诊断中的应用

Application of SVM in Fault Diagnosis for Coal Electromechanical Equipments

针对煤矿大型机电设备故障诊断与维修过程中存在故障数据少、干扰大的非线性特征,提出了采用支持向量机进行故障诊断的方法。建立了系统故障诊断分类模型,采用拉格朗日函数得到了最优解。通过对刮板输送机传动部温度故障数据的参数估计与分类研究,结果表明支持向量机故障诊断效果较好。

An intelligent fault diagnosis method of support vector machine（SVM） is proposed for the problems such as a little fault data and much the non-linear characteristics disturbances in coal large type mine electromechanical equipment fault diagnosis and maintain process.The fault diagnosis classification model is constructed,and the best solution is obtained according to Lagrange theorem.Through the application of the SVM method to the drives speed reducer temperature fault diagnosis and classification of the scraper conveyor,the simulation results show that the proposed SVM method has a good diagnosis effect.