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IRCMFDE based on SORT mapping in fault diagnosis of rotating machinery
Published:2024-01-29 author:WANG Luhong, ZOU Pingji. Browse: 1139 Check PDF documents
IRCMFDE based on SORT mapping in fault diagnosis of rotating machinery


WANG Luhong1,2, ZOU Pingji3

(1.School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China; 

2.Department of Mechatronics Engineering, Changzhi Vocational and Technical College, Changzhi 046000, China; 

3.Department of Personnel, Lanzhou Vocational and Technical College, Lanzhou 730070, China)


Abstract: Aiming at the problem of difficult fault feature extraction caused by the strong nonlinearity and non-stationary nature of vibration signals of rotating machinery, a fault diagnosis method for rotating machinery based on SORT mapping based improved refined composite multiscale fluctuation dispersion entropy (IRCMFDE) method and bat algorithm optimized relevant vector machine (BA-RVM) was proposed. Firstly, the SORT mapping function was used to replace the normal cumulative distribution function of refined composite multiscale fluctuation dispersion entropy (RCMFDE), and the coarsegrained process of RCMFDE method was improved, thus an IRCMFDE method based on SORT mapping was proposed. Then, the fault features of rotating machinery vibration signals were extracted by IRCMFDE method, and the fault feature set was constructed. Finally, BA-RVM classifier was used to intelligently identify and classify the fault types of rotating machinery. The fault diagnosis method based on IRCMFDE and BA-RVM was applied to the experimental data analysis of rolling bearing, centrifugal pump and gear box, and it was compared with the existing fault diagnosis methods. The research results show that the fault diagnosis method based on IRCMFDE and BA-RVM can effectively identify the fault status of rotating machinery, with recognition accuracy rates of 100%, 98%, and 99% respectively, and compared to fault feature extraction methods based on RCMFDE, refined composite multiscale sample entropy, refined composite multiscale fuzzy entropy, refined composite multiscale permutation entropy, and refined composite multiscale dispersion entropy, the efficiency and average recognition accuracy of this method are better than those of the comparison method, and it is more suitable for online real-time fault monitoring of rotating machinery.

Key words: improved refined composite multiscale fluctuation dispersion entropy(IRCMFDE); SORT mapping; bat algorithm optimized relevant vector machine(BARVM); rotating machinery; fault classification and identification
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