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Early degradation monitoring method of rolling bearing based on time-frequency multi-index fusion and graph model

Published:2022-11-22 author:GUI Wei, CHEN Xin, YE Xin-lai. Browse: 469 Check PDF documents

Early degradation monitoring method of rolling bearing based on

timefrequency multiindex fusion and graph model

GUI Wei1, CHEN Xin1, YE Xinlai2,3

(1.School of Mechanical and Electrical Engineering, Wuhan Business University, Wuhan 430100, China;

2.School of Mechanical Engineering, Shandong University, Jinan 250061, China;

3.Weichai Power Co., Ltd., Weifang 261199, China)

Abstract: In order to monitor the whole life cycle health status of rolling bearings, there were some problems such as noise interference and low detection accuracy, when extracting monitoring indicators from vibration signals. A new method for detecting early degradation points of rolling bearings based on time-frequency domain multi-indicator fusion and graph model was proposed.Firstly, multiple time-frequency domain indexes were extracted, the collected bearing original vibration signal was processed by windowing and segmentation, and multiple time-frequency domain indexes were extracted from each signal segment. After extracting the indexes, the graph model was used to model and optimize each index, and a series of graph models were obtained. Then， the similarity of graph model was calculated to get the optimized index. The optimized multi-indexes were fused by the combination of point-to-point mean and point-to-point maximum to obtain a comprehensive monitoring index. Finally, the abnormal decision was made, the early degradation of the bearing was monitored by using the hypothesis test, the degradation point was determined, and the experiment were carried out on the whole life cycle degradation data set of rolling bearing to test the degradation point detection performance of the above comprehensive indexes. At the same time, comparative experiments were carried out with four methods on this data set. The experiment results show that the degradation points can be successfully detected for each group of experimental data, and the highest average ranking of 1.27 is obtained in the comparative experiment. The experimental results show that the method is effective and advanced, and shows that this method has good practical application potential in early monitoring of rolling bearing.

Key words: rotating machinery; life cycle health status monitoring; early degradation points of rolling; vibration signals; time frequency domain index optimization; hypothesis test

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