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Article

A Novel Model for Evaluating the Operation Performance Status of Rolling Bearings Based on Hierarchical Maximum Entropy Bayesian Method

by
Liang Ye
1,
Yusheng Hu
2,3,*,
Sier Deng
1,
Wenhu Zhang
1,
Yongcun Cui
1 and
Jia Xu
2,3
1
School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China
2
State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, Gree Electric Appliances Co., Ltd., Zhuhai 519070, China
3
Guangdong Key Laboratory of Refrigeration Equipment and Energy Conservation Technology, Gree Electric Appliances Co., Ltd., Zhuhai 519070, China
*
Author to whom correspondence should be addressed.
Lubricants 2022, 10(5), 97; https://doi.org/10.3390/lubricants10050097
Submission received: 10 April 2022 / Revised: 3 May 2022 / Accepted: 5 May 2022 / Published: 13 May 2022
(This article belongs to the Special Issue Advances in Bearing Lubrication and Thermal Sciences)
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Abstract

Information such as probability distribution, performance degradation trajectory, and performance reliability function varies with the service status of rolling bearings, which is difficult to analyze and evaluate using traditional reliability theory. Adding equipment operation status to evaluate the bearing operation performance status has become the focus of current research to ensure the effective maintenance of the system, reduce faults, and improve quality under the condition of traditional probability statistics. So, a mathematical model is established by proposing the hierarchical maximum entropy Bayesian method (HMEBM), which is used to evaluate the operation performance status of rolling bearings. When calculating the posterior probability density function (PPDF), the similarities between time series regarded as a weighting coefficient are calculated using overlapping area method, membership degree method, Hamming approach degree method, Euclidean approach degree method, and cardinal approach degree method. The experiment investigation shows that the variation degree of the optimal vibration performance status can be calculated more accurately for each time series relative to the intrinsic series.
Keywords: rolling bearing; performance degradation; variation degree; probability density function; similarities between time series rolling bearing; performance degradation; variation degree; probability density function; similarities between time series

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MDPI and ACS Style

Ye, L.; Hu, Y.; Deng, S.; Zhang, W.; Cui, Y.; Xu, J. A Novel Model for Evaluating the Operation Performance Status of Rolling Bearings Based on Hierarchical Maximum Entropy Bayesian Method. Lubricants 2022, 10, 97. https://doi.org/10.3390/lubricants10050097

AMA Style

Ye L, Hu Y, Deng S, Zhang W, Cui Y, Xu J. A Novel Model for Evaluating the Operation Performance Status of Rolling Bearings Based on Hierarchical Maximum Entropy Bayesian Method. Lubricants. 2022; 10(5):97. https://doi.org/10.3390/lubricants10050097

Chicago/Turabian Style

Ye, Liang, Yusheng Hu, Sier Deng, Wenhu Zhang, Yongcun Cui, and Jia Xu. 2022. "A Novel Model for Evaluating the Operation Performance Status of Rolling Bearings Based on Hierarchical Maximum Entropy Bayesian Method" Lubricants 10, no. 5: 97. https://doi.org/10.3390/lubricants10050097

APA Style

Ye, L., Hu, Y., Deng, S., Zhang, W., Cui, Y., & Xu, J. (2022). A Novel Model for Evaluating the Operation Performance Status of Rolling Bearings Based on Hierarchical Maximum Entropy Bayesian Method. Lubricants, 10(5), 97. https://doi.org/10.3390/lubricants10050097

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