Rolling Bearing Fault Diagnosis Based on Exact Moment Dynamics for Underdamped Periodic Potential Systems

• Published in: IEEE transactions on instrumentation and measurement 2023-01, Vol.72, p.1-1
• Main Authors: Ding, Yamin, Kang, Yanmei, Zhai, Yajie
• Format: Article
• Language: English
• Subjects: Algorithms; Amplification; Background noise; bearing fault diagnosis; Damping; derivative matching closure; Fault detection; Fault diagnosis; Heuristic algorithms; Laboratories; Noise intensity; Noise measurement; Optimization; Parameters; Periodical potential; Roller bearings; Rolling bearings; Stochastic resonance; Transforms
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3253873

This research is to build a more general bridge over the model investigation on stochastic resonance (SR) and the laboratory design in rolling bearing faults. To this end, we generalize the derivative matching moment closure method to the underdamped biased periodic potential systems to disclose the non-monotonic evolution of the spectral amplification factor. With the exact moment dynamics available, a two-layer loop algorithm for detecting the incipient bearing faults is then developed. With the outer loop to optimize the output SNR for the best time scale factor and the inner loop to maximize the spectral amplification factor for optimal system parameter, the semi-analytic results are directly related with this laboratory application. The analog and experimental verification based on different datasets show that the proposed method can perform as well as the existing SR method, even under strong noise background. Particularly, the proposed method does not depend on the amplitude of input signal when optimizing parameters, but only on noise intensity and characteristic fault frequency, thus it has higher detection efficiency than the existing simulation based SR methods.