基于CEEMD-SVD-LSSVM的矿浆管线核心设备故障诊断

Fault diagnosis of core equipment of slurry pipeline based on CEEMD-SVD-LSSVM

针对单向阀振动信号包含背景噪声、故障特征提取困难和诊断准确率不高的问题,提出互补集合经验模态分解(CEEMD)、奇异值分解(SVD)和最小二乘支持向量机(LSSVM)相结合的故障诊断方法.首先,用CEEMD分解单向阀振动信号,并用能量分析法及互相关分析法来选取有用的本征模态函数(IMF).然后,根据SVD法提取相应的故障特征,并输入LSSVM进行故障诊断.通过与集合经验模态分解(EEMD)、支持向量机(SVM)等的比较,表明该方法不仅消除了模态混叠和信号噪声,而且能有效地提取单向阀的故障特征,得到更高的诊断准确率.

In view of the problem that the vibration signal of check valve contains background noise,fault feature extraction is difficult and the diagnostic accuracy is not high,a fault diagnosis method combining complementary ensemble empirical modal decomposition （CEEMD）,singular value decomposition （SVD） and least squares support vector machine （LSSVM） is proposed.Firstly,the vibration signals of check valve are decomposed by CEEMD method,and the useful intrinsic mode functions （IMF） are obtained by energy analysis method and cross correlation analysis method.Then,the corresponding fault features are extracted according to the SVD method and input into the LSSVM for fault diagnosis.Compared with the ensemble empirical modal decomposition （EEMD） and support vector machines （SVM）,this method can not only eliminate the modal aliasing and reduce the background noise,but also can effectively obtain the fault characteristics of the check valve,and finally get a higher diagnostic accuracy rate.