TY - JOUR
T1 - Rolling bearing fault feature extraction using Adaptive Resonance-based Sparse Signal Decomposition
AU - Wang, Kaibo
AU - Jiang, Hongkai
AU - Wu, Zhenghong
AU - Cao, Jiping
N1 - Publisher Copyright:
© 2021 IOP Publishing Ltd
PY - 2021/3
Y1 - 2021/3
N2 - The existence of periodic impacts in collected vibration signal is the representative symptom of rolling bearing localized defect. Due to the complicacy of the working condition, the fault-related impacts are usually submerged in other ingredients. This article proposes an adaptive Resonance-based Sparse Signal Decomposition (RSSD) for extracting the fault features of rolling bearings. Adaptive RSSD is constructed to fetch the impacts from collected vibration signal, by making RSSD decomposed signal kurtosis value maximum using Lion Swarm Algorithm (LSA). Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is further performed to enhance the amplitude and periodicity of impacts contained in RSSD decomposed signal, so that fault feature is highlighted. The superiority and availability of proposed strategy are validated by applying to single fault feature extraction of an experimental dataset and compound faults feature extraction of a locomotive rolling bearing.
AB - The existence of periodic impacts in collected vibration signal is the representative symptom of rolling bearing localized defect. Due to the complicacy of the working condition, the fault-related impacts are usually submerged in other ingredients. This article proposes an adaptive Resonance-based Sparse Signal Decomposition (RSSD) for extracting the fault features of rolling bearings. Adaptive RSSD is constructed to fetch the impacts from collected vibration signal, by making RSSD decomposed signal kurtosis value maximum using Lion Swarm Algorithm (LSA). Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is further performed to enhance the amplitude and periodicity of impacts contained in RSSD decomposed signal, so that fault feature is highlighted. The superiority and availability of proposed strategy are validated by applying to single fault feature extraction of an experimental dataset and compound faults feature extraction of a locomotive rolling bearing.
KW - Adaptive resonance-based sparse signal decomposition
KW - Fault feature extraction
KW - Lion swarm algorithm
KW - Multipoint optimal minimum entropy deconvolution adjusted
KW - Rolling bearing
UR - http://www.scopus.com/inward/record.url?scp=85099778358&partnerID=8YFLogxK
U2 - 10.1088/2631-8695/abb28e
DO - 10.1088/2631-8695/abb28e
M3 - 文章
AN - SCOPUS:85099778358
SN - 2631-8695
VL - 3
JO - Engineering Research Express
JF - Engineering Research Express
IS - 1
M1 - 015008
ER -