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Fault identification model of rotating machinery based on HRFDE and GSA-PNN

Published:2023-12-26 author:HE Dayu, WANG Qiang. Browse: 204 Check PDF documents

Fault identification model of rotating machinery based on

HRFDE and GSA-PNN

HE Dayu1, WANG Qiang2

(1.School of Railway Locomotive, Jilin Railway Technology College, Jilin 132299, China; 2.School of Mechanical

Engineering, Southeast University, Nanjing 210096, China)

Abstract: Aiming at the fluctuation dispersion entropy only extracted a single scale feature, the multi-scale reverse fluctuation dispersion entropy (MRFDE) couldn't extract the high-frequency characteristic signal information, resulting in the defect of the incomplete extracted fault features with the fault identification accuracy of rotating machinery. A rotating machinery fault identification model(diagnosis method)based on hierarchical reverse fluctuation dispersion entropy (HRFDE) and gravity search algorithm optimized probability neural network (GSA-PNN) was proposed accordingly. Firstly, hierarchical segmentation was used to replace the coarse-grained process in MRFDE, and an HRFDE method was proposed to simultaneously extract the low frequency and high frequency information in the vibration signal, thus the low frequency and high frequency information in the fault characteristics of rotating machinery were comprehensively characterized while the fault feature samples were generated. Then, GSA method was used to optimize the smoothness factor of PNN method rapidly, and a GSA-PNN multi-fault classification model was established to recognize and detect the fault types of rotating machinery. Finally, the effectiveness and stability of fault diagnosis method based on HRFDE method and GSA-PNN classifier were experimentally analyzed by using two typical fault data sets of rolling bearing and gearbox, and were compared with existing fault feature extraction methods based on MRFDE, multiscale fluctuation dispersion entropy (MFDE)and hierarchical dispersion entropy(HDE). The results show that the fault diagnosis method based on HRFDE method and GSA-PNN classifier can accurately identify different fault types of rotating machinery, and the recognition accuracy of two data sets reaches 98%. On the basis of sacrificing part of the efficiency, the fault identification accuracy is better than that of the other comparison methods, and the comprehensive performance is better.

Key words: rotating machinery; reverse fluctuation dispersion entropy(RFDE); hierarchical reverse fluctuation dispersion entropy(HRFDE); fault classifier; gravity search algorithm; probability neural network(PNN)

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