摘要详情

ID / 提交时间

99 / 2023-09-19 16:58:06

标题

An Improved Shift-Invariant Sparse Bearing Fault Diagnosis Method Based on Feature Learning Base Atoms

关键字

bearing fault diagnosis,feature learning,based atom,shift-invariant sparse

主题及专题

Compressive Sensing & Sparse Decomposition

状态

终稿

作者

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

摘要

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.