AN ENHANCED TEAGER HUANG TRANSFORM TECHNIQUE FOR BEARING FAULT DETECTION
Keywords:
Rolling Element Bearings, Fault Detection, Transform, Signal ProcessingAbstract
Rolling element bearings are widely used in rotating machinery. Bearing health condition monitoring plays a vital role in predictive maintenance to recognize bearing faults at an early stage to prevent machine performance degradation, improve operation quality, and reduce maintenance costs. Although many fault detection techniques have been proposed in literature for bearing fault diagnosis, reliable bearing fault detection still remains challenging. This study aims to propose an enhanced Teager-Huang transform (eTHT) technique for bearing fault detection. The eTHT takes the several processing steps: Firstly, an empirical mode decomposition analysis is undertaken to recognize the most representative intrinsic mode functions (IMFs). Secondly, a correlation characteristic function is suggested to formulate the analytical signal. Thirdly, the averaged Teager-Kaiser spectrum analysis is undertaken to identify the representative features for bearing fault detection. The effectiveness of the proposed eTHT technique is examined by experimental tests corresponding to different bearing conditions.
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Case Western Reserve University, Bearing Data Center, 2024 [Online available] https://csegroups.case.edu/bearingdatacenter/pages/apparatus-procedures.
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Copyright (c) 2024 Zihao Chen, Andrew Kadik, Wilson Wang (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
