| Citation: | YU Yang, LI Yun, YANG Ping, et al. Feature extraction of AE signal for rolling bearing fault by improved TFCA method[J]. Journal of Aerospace Power, 2024, 39（12）：20220318 doi: 10.13224/j.cnki.jasp.20220318
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In order to solve the problem of traditional time-frequency coherence analysis (TFCA) method's failure to analyze the single-channel signal, a new time-frequency coherence method was proposed. The new method can analyze the single-channel signal through the average estimation operator. The soft-land full-information acoustic emission (AE) system and the rotating machinery simulation platform were used to detect the inner ring and outer ring faults of the rolling bearing. The study focused on the extraction of fault characteristic periods of rolling bearings under conditions of low signal-to-noise ratio. The proposed method was compared with the traditional time-frequency coherence, short-time Fourier transform, wavelet coherence methods, and was verified experimentally. The simulated and experimental results showed that the proposed method is superior to other time-frequency methods. The proposed method can accurately extract the period characteristics of fault signal, and exhibit high time-frequency aggregation and strong anti-noise performance.
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