TY - JOUR
T1 - Adaptive multiscale wavelet-guided periodic sparse representation for bearing incipient fault feature extraction
AU - Niu, Mao Gui
AU - Jiang, Hong Kai
AU - Yao, Ren He
N1 - Publisher Copyright:
© Science China Press 2024.
PY - 2024/11
Y1 - 2024/11
N2 - Currently, accurately extracting early-stage bearing incipient fault features is urgent and challenging. This paper introduces a novel method called adaptive multiscale wavelet-guided periodic sparse representation (AMWPSR) to address this issue. For the first time, the dual-tree complex wavelet transform is applied to construct the linear transformation for the AMWPSR model. This transform offers superior shift invariance and minimizes spectrum aliasing. By integrating this linear transformation with the generalized minimax concave penalty term, a new sparse representation model is developed to recover faulty impulse components from heavily disturbed vibration signals. During each iteration of the AMWPSR process, the impulse periods of sparse signals are adaptively estimated, and the periodicity of the latest sparse signal is augmented using the final estimated period. Simulation studies demonstrate that AMWPSR can effectively estimate periodic impulses even in noisy environments, demonstrating greater accuracy and robustness in recovering faulty impulse components than existing techniques. Further validation through research on two sets of bearing life cycle data shows that AMWPSR delivers superior fault diagnosis results.
AB - Currently, accurately extracting early-stage bearing incipient fault features is urgent and challenging. This paper introduces a novel method called adaptive multiscale wavelet-guided periodic sparse representation (AMWPSR) to address this issue. For the first time, the dual-tree complex wavelet transform is applied to construct the linear transformation for the AMWPSR model. This transform offers superior shift invariance and minimizes spectrum aliasing. By integrating this linear transformation with the generalized minimax concave penalty term, a new sparse representation model is developed to recover faulty impulse components from heavily disturbed vibration signals. During each iteration of the AMWPSR process, the impulse periods of sparse signals are adaptively estimated, and the periodicity of the latest sparse signal is augmented using the final estimated period. Simulation studies demonstrate that AMWPSR can effectively estimate periodic impulses even in noisy environments, demonstrating greater accuracy and robustness in recovering faulty impulse components than existing techniques. Further validation through research on two sets of bearing life cycle data shows that AMWPSR delivers superior fault diagnosis results.
KW - dual-tree complex wavelet transform
KW - generalized minimax concave penalty
KW - incipient fault feature extraction
KW - periodic sparse representation
UR - http://www.scopus.com/inward/record.url?scp=85207338761&partnerID=8YFLogxK
U2 - 10.1007/s11431-024-2774-2
DO - 10.1007/s11431-024-2774-2
M3 - 文章
AN - SCOPUS:85207338761
SN - 1674-7321
VL - 67
SP - 3585
EP - 3596
JO - Science China Technological Sciences
JF - Science China Technological Sciences
IS - 11
ER -