A Reinforced Noise Resistant Correlation Method for Bearing Condition Monitoring

作者：Fan, Wei; Chen, Zhenqiang; Li, Yongxiang^*; Zhu, Feng; Xie, Min

来源：IEEE Transactions on Automation Science and Engineering, 2023, 20(2): 995-1006.

DOI：10.1109/TASE.2022.3177010

• Degradation
• Condition monitoring
• Feature extraction
• Correlation
• Interference
• Vibrations
• Monitoring
• feature extraction
• health index
• incipient fault detection
• rolling bearings

Condition monitoring plays a significant role in guaranteeing the reliability and safety of rotating machinery, which aims to detect an incipient fault and assess the degradation tendency. The construction of a health index (HI) is a crucial step to realize above tasks. At present, kurtosis, crest factor, and so on have been recognized as popular HIs to depict the operating condition. However, shortcomings of these classical HIs still exist: 1) classical HIs are prone to be affected by strong white Gaussian noise; 2) classical HIs are not sensitive to incipient faults. To deal with these two shortcomings, a reinforced noise resistant correlation method is proposed in this paper. Firstly, a new signal is constructed using the steps of segmenting and averaging to suppress the interference of noise. Then, a novel correlation function is used to get the hidden period. The proposed HI is constructed based on the discrete version of this correlation function to increase the sensitivity of incipient faults. Subsequently, theoretical values of the proposed HI under healthy states are investigated. The effectiveness of the method is demonstrated using simulated degradation processes and two accelerated degradation datasets of rolling element bearings. Through comparisons with other classical HIs, the proposed HI can simultaneously suppress the interference of strong noise and detect incipient faults. The comparison results identify the effectiveness of the proposed method in monitoring the condition of rotating machinery.

• 单位
  上海交通大学; 江苏大学