期刊
ENERGIES
卷 13, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/en13051094
关键词
rolling bearing; fault prediction; quantum particle swarm optimization; backpropagation neural network; Dempster-Shafer evidence theory
资金
- National Natural Science Foundation for Young Scientists of China [61702177]
- Open Platform Innovation Foundation of Hunan Provincial Education Department [17K029]
- Natural Science Foundation of Hunan Province, China [2019JJ60048]
- National Key Research and Development Project [2018YFB1700204, 2018YFB1003401]
- Key Research and Development Project of Hunan Province [2019GK2133]
To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster-Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster-Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.
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