Bearing Fault Feature Extraction Method Based on GA-VMD and Center Frequency

• Published in: Mathematical problems in engineering Vol. 2022; pp. 1 - 19
• Main Authors: Li, Yuxing, Tang, Bingzhao, Jiang, Xinru, Yi, Yingmin
• Format: Journal Article
• Language: English
• Published: New York Hindawi 30.01.2022; John Wiley & Sons, Inc
• Subjects: Bearings; Decomposition; Entropy; Fault diagnosis; Feature extraction; Feature recognition; Genetic algorithms; K-nearest neighbors algorithm; Mathematical analysis; Noise; Optimization algorithms; Parameters; Particle swarm optimization; Population; Signal processing
• ISSN: 1024-123X; 1026-7077; 1563-5147; 1563-5147
• DOI: 10.1155/2022/2058258

To promote the effect of variational mode decomposition (VMD) and further enhance the recognition performances of bearing fault signals, genetic algorithm (GA) is applied to optimize the combination of VMD parameters in this paper, and GA-VMD algorithm is put forward to improve the decomposition accuracy of VMD. In addition, combined with the center frequency, a feature extraction method based on GA-VMD and center frequency is proposed to ameliorate the difficulty of bearing fault feature extraction. Firstly, the bearing signal is decomposed into a series of intrinsic mode components (IMFs) by GA-VMD. Then, the Center Frequency of IMFs is extracted, and the recognition rate is calculated by k-nearest neighbor (KNN) algorithm. Simulation signal experiments state clearly that, compared with manual parameter setting-VMD algorithm and parameter optimization VMD algorithm based on particle swarm optimization (PSO), the decomposition result of GA-VMD has a smaller root mean square error and higher decomposition accuracy, which verifies the effectiveness of GA-VMD. The experimental results demonstrate that, by comparison with the feature extraction method based on envelope entropy, the feature extraction method based on center frequency has better inter class separability and higher mean recognition rate (the highest recognition rate of single feature is 94.5%, and in the case of multiple features, the recognition rate reaches 100% when four features are extracted) and can realize the accurate identification of different bearing fault signals.