| Citation: | TIAN Jing, ZHANG Yuwei, ZHANG Fengling, et al. Inter-shaft bearing fault diagnosis method based on generalized refined composite multiscale quantum entropy and kernel principal component analysis[J]. Journal of Aerospace Power, 2024, 39（2）：20210467 doi: 10.13224/j.cnki.jasp.20210467
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In view of the problems of complex paths of inter-shaft bearing vibration signal transmission to the measurement surface of the magazine, which lead to difficulties in fault feature extraction and identification, a fault diagnosis method based on generalized refined composite multiscale quantum entropy (GRCMQE), kernel principal component analysis (KPCA) and parameter optimization support vector machine was proposed for inter-shaft bearing fault diagnosis. Firstly, GRCMQE was used to extract fault features from vibration signals, and high-dimensional fault feature sets were constructed. Secondly, KPCA method was utilized to reduce the dimension of high-dimensional feature data to obtain low dimensional manifold features. Then, the obtained features were input into the support vector machine based on cross validation to complete the fault pattern recognition. Finally, the proposed method was tested on the intermediate bearing fault data set, and the results showed that the method can effectively identify different fault types of intermediate bearing, with the fault identification accuracy up to 98.33%.
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