基于HGWO-MSVM的采煤机滚动轴承故障诊断方法

摘要

针对采煤机滚动轴承故障特征向量提取较困难、多分类效果不理想等问题,提出了基于HGWO-MSVM的采煤机轴承故障诊断方法.对轴承故障信号进行小波降噪处理,利用经验模态分解算法对降噪后信号进行分解,并提取能量特征值,作为MSVM的训练集和测试集.采用MSVM进行故障状态识别,并用HGWO算法对MSVM的参数进行优化.试验结果表明,相比于GWO、GA和PSO优化MSVM模型,基于HGWO-MSVM的采煤机轴承故障诊断模型可明显提高故障识别精度和效率.%In view of problems of difficult extracting of fault feature vector and unsatisfactory multiclassification effect of shearer rolling bearing,a fault diagnosis method for rolling bearing of shearer based on HGWO-MSVM was proposed.The bearing fault signal is denoised by wavelet and decomposed by empirical mode decomposition algorithm,then energy characteristic value is extracted and used as training set and test set of MSVM.The MSVM is used to identify fault status and parameters of MSVM are optimized by HGWO algorithm.The experimental results show that the fault diagnosis model of shearer bearing based on HGWO-MSVM can obviously improve accuracy and efficiency of fault identification compared with GWO,GA and PSO optimization MSVM model.