Journal
MEASUREMENT
Volume 140, Issue -, Pages 240-253Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2019.03.061
Keywords
Cluster-contraction Stage-wise Orthogonal-Matching-Pursuit (CcStOMP) algorithm; Sparse representation; K-nearest neighbor (KNN); Bearing fault diagnosis; Transient feature extraction
Funding
- National Natural Science Foundation of China [51575102]
- Six talent peaks project in Jiangsu Province [JXQC-003]
- Fundamental Research Funds for the Central Universities of China [2242017K40112]
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This paper presents a novel Cluster-contraction Stage-wise Orthogonal-Matching-Pursuit (CcStOMP) approach for bearing fault information extraction. This approach adds the cluster contraction mechanism to the Stage-wise Orthogonal-Matching-Pursuit (StOMP) algorithm and filter the selected atoms twice during atomic search, which makes the condition number of the support set more reasonable, thus realizing amelioration of the pathological equation in weight determination. It can improve the accuracy of sparse recovery while maintaining the rapid convergence characteristic, with good robustness. Both simulation and experimental studies have verified that the proposed CcStOMP approach can extract bearing fault feature component more precisely compared with StOMP, thus improving the accuracy of fault diagnosis. (C) 2019 Elsevier Ltd. All rights reserved.
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