An intelligent fault diagnosis method for rolling bearings based on hybrid characteristics

To address the problem of low accuracy of fault diagnosis using traditional feature indexes, a new intelligent fault diagnosis method for rolling bearings was proposed based on a hybrid scale exponent index and improved support vector machine. First, the scale exponent index for indicating fault was obtained by using the super order analysis, and the hybrid characteristic index matrix was 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) was used to classify the constructed mixed vectors, and particle swarm optimization algorithm was used to optimize the important parameters of SVM. Finally, the proposed intelligent fault diagnosis method for rolling bearing was verified by using the rolling bearing test bench. 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 bearings. © 2022 Chinese Vibration Engineering Society. All rights reserved.