VMD combined with wavelet packet information entropy and GJO-SVM for motor bearing fault diagnosis
To address the problem of low diagnostic accuracy due to the difficulty in extracting fault features of rolling bearings in electric motors,a feature extraction method based on Variational Modal Decomposition(VMD)combined with Wavelet Packet In-formation Entropy(WPIE)is proposed.A Support Vector Machine(SVM)optimized by Golden Jackal Optimization(GJO)is used for the fault diagnosis of motor bearings.Firstly,the collected signal is decomposed by VMD and the optimal eigenmode component Intrinsic Mode Function(IMF)is filtered based on the local minimal envelope entropy;secondly,the wavelet packet is decomposed again and the information entropy is extracted as the feature vector matrix;finally,the penalty and kernel parameters in the support vector machine are optimally selected by the GJO algorithm,and the GJO-SVM fault diagnosis model is estab-lished,the feature vector matrix is input into Golden Jackal Optimizes algorithm the Support Vector Machine(GJO-SVM)for fault diagnosis.The VMD combined with wavelet packet information entropy feature extraction is compared with VMD combined with approximate entropy feature extraction,and the experimental results show that the accuracy of VMD combined with wavelet information entropy feature extraction is improved by 2.5％,and its feature extraction is more superior.The experimental results show that the average accuracy of GJO-SVM reaches 99.16％,which is 2.5％and 3.61％higher than that of PSO-SVM and FOA-SVM respectively.GJO-SVM can extract and diagnose bearing faults more effectively.
万方数据
维普
