Abstract：To address the problem of low accuracy of fault diagnosis using traditional feature indexes, a new intelligent fault diagnosis method of rolling bearing is proposed based on hybrid scale exponent index and improved support vector machine. First, the scale exponent index for indicating fault is obtained by using the super order analysis, and the hybrid characteristic index matrix is constructed by combining it with the conventional characteristic indexes, so as to improve the discrimination of the characteristic index to the fault. Second, Support Vector Machine (SVM) is used to classify the constructed mixed vectors, and particle swarm optimization algorithm is used to optimize the important parameters of SVM. Finally, the proposed intelligent fault diagnosis method of rolling bearing is verified by using the rolling bearing test bench. The results show that the training accuracy and testing accuracy using the hybrid feature indexes are improved by 13% and 23%, respectively, compared with the conventional feature indexes. The proposed method can not only identify the fault types, but also identify the damage degree of the same fault, which emerges to further realize the quantitative fault diagnosis of rolling bearing.
Key words: Intelligent fault diagnosis; Super-order analysis; Hybrid characteristic indexes; Particle swarm optimization; Support vector machine
陆建涛，姚通，李舜酩，崔荣庆. 混合特征驱动的滚动轴承智能故障诊断方法[J]. 振动与冲击, 2022, 41(16): 79-84.
LU Jiantao，YAO Tong，LI Shunming，CUI Rongqing. An intelligent fault diagnosis method for rolling bearings based on hybrid characteristics. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(16): 79-84.
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