A post-processing method called Fourier transform based on local maxima of autocorrelation function for extracting fault feature of bearings

• Published in: Advanced engineering informatics Vol. 62; p. 102766
• Main Authors: Liu, Tao, Li, Xinsan, Sun, Junshuai, Lyu, Mindong, Yan, Shaoze
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2024
• Subjects: Fault feature; Fourier transform; Local maxima; Repetitive impulses; Short-time Fourier transform; Unbiased autocorrelation function; Repetitive impulses; Fourier transform; Unbiased autocorrelation function; Fault feature; Local maxima; Short-time Fourier transform
• ISSN: 1474-0346
• DOI: 10.1016/j.aei.2024.102766

Accurate extraction of fault features from signals containing weak repetitive pulses is a critical issue in fault diagnosis of bearings. To address this challenge, a novel solution termed Fourier transform based on Local Maxima (FT-LM) is proposed in this paper. In the FT-LM method, local maxima are first extracted from signals, followed by the application of a Fourier transform to this sequence. Furthermore, to enhance the detection of repetitive pulses, the Short-Time Fourier Transform (STFT) and the unbiased autocorrelation function are integrated into the FT-LM, developing a post-processing algorithm called Fourier Transform based on Local Maxima of Autocorrelation Function (FT-LMACF). By employing a series of simulated signals and two sets of public experimental data, comparisons are made among the FT-LMACF, mainstream post-processing methods, the Kurtogram method and the spectral coherence based on the STFT. The results indicate that the FT-LMACF outperforms other algorithms in extracting fault features inherent in repetitive pulses.