基于LS-SVM的一次风机振动在线监测及故障预警研究

针对火电厂一次风机运行工况复杂和多状态变量强耦合特性而难以构建设备精确模型问题,将智能数据挖掘方法应用于风机设备故障预警和诊断中。通过对风机典型运行特性进行分析,提出了一种基于最小二乘支持向量机(LS-SVM)的一次风机振动状态估计和故障预警方法。结合山西河曲发电厂1号机组的1#一次风机历史运行数据,应用Matlab对所提出的方法进行了验证和分析。研究结果表明,该预测方法有较高的估计精度,能够及时辨别一次风机在运行中的振动异常,适用于火电厂辅机设备的故障诊断,具有一定的工程应用价值。

Research of LS-SVM based method for online monitoring and fault prediction of primary air fan vibration

Aiming at the problem of real-time monitoring and fault diagnosis of primary air fan in thermal power plant, a data mining based Least Squares Support Vector Machine( LS-SVM) primary air fan vibration estimation and fault early warning method was proposed due to it difficult to achieve fault diagnosis through the precise mechanism modeling because of the complex, changeable operation and the cross-coupling process variables of the auxiliary equipment. The historical operation data of the 1# primary air fan of unit 1 in Hequ power plant was tested. The results indicate that the method has high estimation accuracy, and can identify the abnormal vibration of the primary air fan in time. The validity of the method is verified. [ABSTRACT FROM AUTHOR]