Abstract:
According to the rolling bearing fault information is relatively weak and there are interference signals, the rolling bearing fault diagnosis method of Local Meanv Decomp...Show MoreMetadata
Abstract:
According to the rolling bearing fault information is relatively weak and there are interference signals, the rolling bearing fault diagnosis method of Local Meanv Decomposition (LMD) and Tunable-Q Wavelet Transform (TQWT) is proposed. The signal is decomposed using LMD to obtain a finite number of product function components; then the product function components obtained from the decomposition are filtered and reconstructed using the cliff criterion; TQWT decomposition based on Artificial Bee Colony Algorithm (ABC) is performed for the reconstructed signal, and ABC algorithm is used to optimize the high and low quality factors in the TQWT decomposition; Finally, envelope spectral analysis is carried out on the low-resonance components obtained from the decomposition to extract the eigenfrequency of the fault signal. Further comparison proves the feasibility of LMD-ABC-TQWT and applies it to rolling bearing inner and outer ring fault diagnosis. The evaluation metrics prove that the proposed method can extract bearing weak faults more efficiently.
Published in: 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)
Date of Conference: 22-24 September 2023
Date Added to IEEE Xplore: 03 November 2023
ISBN Information: