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| Fault diagnosis of rolling bearings based on locally joint sparse marginal embedding |
| ZHOU Hongdi，ZHANG Hang，ZHONG Fei |
| School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China |
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Abstract Rolling bearing is one of the important parts of machinery and equipment, and it is great significance to the safe operation of machinery and equipment through timely and effective monitoring and diagnosis. In order to address the high dimensionality and information redundancy caused by information fusion in existing bearing fault diagnosis methods, a novel bearing defection diagnosis method based on Locally Joint Sparse Marginal Embedding (LJSME) was proposed. LJSME uses L_2,1 -norm to reconstruct within-class and between-class matrices and introduces local graphs to preserve the neighborhood relations among high-dimensional features, and uses L_2,1 -norm as the regular term of the objective function to obtain the joint sparsity of feature extraction, thus ensuring the effectiveness of feature extraction. The method first extracts a high-dimensional feature dataset consisting of time-domain and frequency-domain information from the bearing vibration signal; then extracts sensitive low-dimensional features in the high-dimensional feature space dataset using LJSME; and finally achieves the fault pattern recognition of rolling bearings using a K-nearest neighbor classifier. The proposed method is validated by two sets of rolling bearing fault datasets, and the proposed algorithm can effectively extract sensitive features of rolling bearing vibration signals compared with other three dimensionality reduction algorithms.
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Received: 04 May 2022
Published: 28 July 2023
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