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| Rolling Bearing Fault Diagnosis Based on Ensemble Empirical Mode Decomposition and K-Singular Value Decomposition Dictionary Learning |
| LI Ji-meng,LI Ming,YAO Xi-feng,WANG Hui,YU Qing-wen,WANG Xiang-dong |
| College of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract The morphology of atom in dictionary constructed by the K-singular value decomposition algorithm is affected by noise and harmonic interference, which reduces the extraction precision of the fault feature. To solve this problem, a method of impulse feature extraction based on ensemble empirical mode decomposition and K-singular value decomposition dictionary learning is proposed to realize the fault diagnosis of rolling bearing. Firstly, ensemble empirical mode decomposition and Hurst exponent are used to preprocess the vibration signal to remove the harmonic interference; Then, the preprocessed signal K-singular value decomposition algorithm is learned by K-singular value decomposition algorithm to construct the over-complete dictionary; Next, the dictionary is analyzed K-means clustering algorithm to remove the atoms with smaller kurtosis values; Finally, the orthogonal matching pursuit algorithm is used to realize the sparse representation of impulse fault features. Experiments analysis and engineer application verify the effectiveness and practicability of the proposed method.
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Received: 22 January 2019
Published: 10 October 2020
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2020, Vol. 41 
