Abstract:A hybrid intelligent mechanical fault diagnosis method based on probability box theory and improved Grey Wolf algorithm to optimize support vector machine was proposed to solve the problem of information loss, misoperation and other uncertainties in feature extraction of rolling bearing fault vibration signal and the problem of poor accuracy of fault diagnosis.Firstly, the probability box is obtained by direct modeling method, and then its features are extracted by cumulative uncertainty measurement method to construct the feature vector set for fault diagnosis.Secondly, the improved Grey Wolf algorithm is used to optimize the support vector machine.Finally, the feature set is classified and diagnosed by using the optimized support vector machine.The proposed method makes full use of the advantages of probability box in dealing with uncertain problems and the excellent classification performance of support vector machine in solving small sample and nonlinear pattern recognition, so that vibration signals of different fault types can be more accurately identified.Experimental verification and comparative analysis of rolling bearing vibration signal show that this method is effective in fault diagnosis of rolling bearing.
路小娟,石成基. 一种基于概率盒—HGWO优化SVM的滚动轴承故障诊断方法[J]. 振动与冲击, 2021, 40(22): 234-241.
LU Xiaojuan, SHI Chengji. Application of the P-box theory and HGWO-SVM in the fault diagnosis of rolling bearings. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(22): 234-241.
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