报告详情
A Fault-Mechanism Knowledge Integration-Based Method for Constructing Incipient Fault Early Warning Indicators in Bearings
编号：106
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更新：2025-11-10 15:32:16 浏览：15次
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摘要
Bearing fault early warning methods based on signal characteristic values are commonly used for condition monitoring of industrial equipment. However, challenges such as the difficulty in establishing multi-parameter threshold warning rules, the presence of false alarms and missed detections, and the requirement for extensive experience in signal analysis continue to pose obstacles to accurate equipment early warning. Therefore, this paper proposes a construction methodology for bearing health warning indicators that integrates knowledge of fault mechanisms, providing an application foundation for machinery condition early warning. Firstly, based on the mechanisms underlying various bearing fault types, the patterns of time-frequency signal characteristics are analyzed to identify the behavioral patterns of multiple feature indicators for major fault types. Secondly, a fused bearing health indicator termed Health Integration Indicators (HII) is constructed based on the average weighting of envelope harmonic energy amplitude ratios. The 3σ upper threshold warning model using baseline values from healthy data is established to distinguish between the healthy and warning states of bearing operation. Then, the relative influence weight of each characteristic index on HII is quantified to help confirm the fault type of the bearing and realize the interpretable auxiliary diagnosis of fault type. Finally, validation is conducted using the IMS Bearing Data and the Case Western Reserve University (CWRU) dataset. Experimental results demonstrate that the proposed indicator offers more robust analytical outcomes and quantitative justification than warning methods based on kurtosis and RMS values.
关键词
Fault mechanism, Health integration indicators, Condition monitoring, Early warning, Fault diagnosis
报告人
Changkun Han
Engineer Beizisuo (Beijing) Technology Development Co., Ltd Beijing, China稿件作者
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重要日期
-
会议日期
11月21日
2025
至11月23日
2025
-
10月20日 2025
初稿截稿日期
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11月23日 2025
注册截止日期
主办单位
IEEE Instrumentation and Measurement Society
South China University of Technology
South China University of Technology
承办单位
South China University of Technology
联系方式
- Icsmd
- ic******@163.com
历届会议
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2023年11月02日 中国 China
International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence -
2023年11月02日 中国 Xi'an
2023 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence -
2022年11月30日 中国 Harbin
International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence -
2021年10月21日 中国 Nanjing
2021 International Conference on Sensing, Measurement and Data Analytics in the era of Artificial Intelligence -
2020年10月15日 中国 Xi'an
International Conference on Sensing, Measurement and Data Analytics in the era of Artificial Intelligence
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