TY - JOUR
T1 - Incipient bearing fault detection using adaptive fast iterative filtering decomposition and modified Laplace of Gaussian filter
AU - Wei, Yu
AU - Li, Yongbo
AU - Wang, Xianzhi
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2025/3
Y1 - 2025/3
N2 - The impact components induced by faulty bearings can be readily concealed by environmental noise and other interferences due to their inherent weakness, especially during the incipient stages of fault development. A novel approach is presented in this study for the detection of incipient bearing faults, which combines an adaptive fast iterative filtering decomposition (FIFD) method with a modified Laplace of Gaussian filter. The first step involves proposing an adaptive FIFD (AFIFD) method employing improved sparrow search algorithm, enabling adaptive selection of the optimal parameter within the FIFD method. The AFIFD technique is able to adaptively decompose a complicated signal into a set of mono-components. Subsequently, a modified Laplace of Gaussian is used to highlight the fault-related cyclic impulse train from a sensitive mono-component decomposed by the AFIFD method. Finally, the envelope analysis performing on enhanced signals is applied to identify fault characteristic frequencies. Results from some case studies demonstrate that the proposed method is capable of extracting incipient fault signatures. The superiority of the proposed method is further validated through some comparative tests with recently developed fault detection methods.
AB - The impact components induced by faulty bearings can be readily concealed by environmental noise and other interferences due to their inherent weakness, especially during the incipient stages of fault development. A novel approach is presented in this study for the detection of incipient bearing faults, which combines an adaptive fast iterative filtering decomposition (FIFD) method with a modified Laplace of Gaussian filter. The first step involves proposing an adaptive FIFD (AFIFD) method employing improved sparrow search algorithm, enabling adaptive selection of the optimal parameter within the FIFD method. The AFIFD technique is able to adaptively decompose a complicated signal into a set of mono-components. Subsequently, a modified Laplace of Gaussian is used to highlight the fault-related cyclic impulse train from a sensitive mono-component decomposed by the AFIFD method. Finally, the envelope analysis performing on enhanced signals is applied to identify fault characteristic frequencies. Results from some case studies demonstrate that the proposed method is capable of extracting incipient fault signatures. The superiority of the proposed method is further validated through some comparative tests with recently developed fault detection methods.
KW - Incipient bearing fault detection
KW - fast iterative filtering decomposition
KW - improved sparrow search algorithm
KW - modified Laplace of Gaussian
UR - http://www.scopus.com/inward/record.url?scp=85192135034&partnerID=8YFLogxK
U2 - 10.1177/14759217241246985
DO - 10.1177/14759217241246985
M3 - 文章
AN - SCOPUS:85192135034
SN - 1475-9217
VL - 24
SP - 909
EP - 924
JO - Structural Health Monitoring
JF - Structural Health Monitoring
IS - 2
ER -