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99 / 2023-09-19 16:58:06
An Improved Shift-Invariant Sparse Bearing Fault Diagnosis Method Based on Feature Learning Base Atoms
bearing fault diagnosis,feature learning,based atom,shift-invariant sparse
Compressive Sensing & Sparse Decomposition
Final Paper
Changkun Han / Beijing University of Chemical Technology
wei lu / Beijing University of Chemical Technology；Institute of Engineering Technology, Sinopec Catalyst Company Limited Beijing, China
Hongjie Zhang / Beijing University of Chemical Technology
Liuyang song / Beijing university of chemical technology
Huaqing Wang / Beijing university of chemical technology
Bearings, as crucial components in the power systems of high-end equipment, are susceptible to noise and other redundant elements when faults occur. In this paper, an improved shift-invariant sparse (ISiS) feature extraction method based on the feature learning base atom (FLBA) is proposed to extract the weak features in the fault signal. First, a low-dimensional learning dictionary consisting of base atoms is acquired by shift-invariant K-SVD learning. The FLBA selection method based on the kurtosis criterion is proposed to select the dictionary base atom with the largest kurtosis value for matching the feature information in the signal. Subsequently, an ISiS model is proposed, which aims to minimize the iteration residuals, thus reducing redundant iterations at the same index position and improving the parsing ability of features. Simulations and experiments are conducted to verify the effectiveness of the algorithm. In addition, the model is compared with the conventional fast spectral kurtosis method, thus confirming the advantages of the proposed model.

Bearings, as crucial components in the power systems of high-end equipment, are susceptible to noise and other redundant elements when faults occur. In this paper, an improved shift-invariant sparse (ISiS) feature extraction method based on the feature learning base atom (FLBA) is proposed to extract the weak features in the fault signal. First, a low-dimensional learning dictionary consisting of base atoms is acquired by shift-invariant K-SVD learning. The FLBA selection method based on the kurtosis criterion is proposed to select the dictionary base atom with the largest kurtosis value for matching the feature information in the signal. Subsequently, an ISiS model is proposed, which aims to minimize the iteration residuals, thus reducing redundant iterations at the same index position and improving the parsing ability of features. Simulations and experiments are conducted to verify the effectiveness of the algorithm. In addition, the model is compared with the conventional fast spectral kurtosis method, thus confirming the advantages of the proposed model.
Important Date

  • Conference Date

    Nov 02

    2023

    to

    Nov 04

    2023

  • Dec 15 2023

    Draft paper submission deadline

  • Dec 20 2023

    Registration deadline

Sponsored By
IEEE Instrumentation and Measurement Society
Xidian University
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