Bearing Fault Diagnosis Based on VMD Energy Entropy and Optimized Support Vector Machine
JIN Jiang-tao1,XU Zi-fei1,LI Chun1,2,MIAO Wei-pao1,LI Gen1
1. Energy and Power Engineering Institute, University of Shanghai for Science and Technology, Shanghai 200093, China
2. Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, Shanghai 200093, China
Abstract The early fault signals of rolling bearings are relatively weak, and are affected by the coupling of noise and vibration, which leads to inaccurate fault judgments. Based on variational mode decomposition (VMD) and energy entropy, multi-mode characteristic matrix is constructed. Grey wolf optimizer (GWO) is adopted to optimize the parameters of support vector machine (SVM). VMD-Entropy-OSVM bearing intelligent fault diagnosis is proposed, using bearing experimental data to verify the effectiveness and superiority of the proposed method. The experimental results show that VMD-Entropy-OSVM not only recognizes different fault types at the end of bearing damage, but also has high accuracy at the beginning of bearing damage. The accuracy of the proposed method is up to 99.8% at 8dB, which is 3.3%～27.3% higher than the existing method. When the SNR is 0dB, the accuracy is still 73.5%, which is 11%～33% higher than the existing method, the model shows good generalization performance. In addition, the running time is shorter and more efficient under the same computing resources.
JIN Jiang-tao,XU Zi-fei,LI Chun, et al. Bearing Fault Diagnosis Based on VMD Energy Entropy and Optimized Support Vector Machine[J]. Acta Metrologica Sinica, 2021, 42(7): 898-905.
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2021, Vol. 42 
