基于MFCC特征和GWO-SVM的托辊故障诊断

针对目前带式输送机托辊故障诊断方法存在接触式测量、准确率低、井下大范围检测困难等问题，提出了一种基于MFCC特征和参数优化SVM的托辊故障诊断方法。利用变分模态分解(VMD)将采集到的托辊声音信号分解为若干本征模态分量(IMF)，并基于包络熵和峭度组成的复合指标优选IMF分量；提取所选分量的梅尔倒频谱系数（MFCC)作为特征，利用灰狼优化算法（GWO）优化SVM参数；将样本特征向量输入GWO-SVM中进行故障分类。结果表明:对于正常托辊、托辊内圈故障、托辊外圈故障、托辊卡死4种工况，该方法故障识别平均准确率在95%以上。与单一指标相比，复合指标提取的IMF分量故障特性代表性更好；与其他优化算法相比，该方法的识别准确率更高，分类速度更快。

Roller Fault Diagnosis Based on MFCC Feature and GWO-SVM

In order to solve the problems of current belt conveyor roller fault diagnosis methods, such as contact measurement, low accuracy and difficulty in large-scale underground detection, a fault diagnosis method for roller was proposed based on MFCC feature and parameter optimization SVM. The collected roller sound signals were decomposed into several intrinsic mode function (IMF) by using variational mode decomposition (VMD), and the IMF component was optimized based on the composite index of envelope entropy and kurtosis. Mel frequency cepstrum coefficients (MFCC) of selected components were extracted as features, and grey wolf optimizer (GWO) was used to optimize SVM parameters. The sample feature vectors were input into GWO-SVM for fault classification. The results show that the average accuracy of the method is more than 95% for the four working conditions of normal roller, roller with inner ring fault, roller with outer ring fault and roller stuck. Compared with single index, the fault characteristics of IMF components extracted by composite index are more representative. Compared with other optimization algorithms, this method has higher recognition accuracy and faster classification speed.