Staged life prediction of rolling bearing based on improved GA_BP neural network
Pages 85 - 92
Abstract
Aiming at the problem that the health status of rolling bearings is difficult to evaluate and the remaining life is difficult to estimate, a method to optimize BP neural network based on improved genetic algorithm is proposed. Under the limited state, the characteristic parameters representing bearing performance degradation are extracted and selected to be fused to obtain the bearing performance degradation trend, and the quantitative division of the rolling bearing state is realized. On the basis of the division, the BP neural network model is used for life prediction. Considering the premature problem of the model, an improved adaptive method is designed to dynamically calculate genetic operators, that is, the crossover rate and mutation rate are adjusted during the adaptive solution process. The experimental results show that this method can effectively achieve the prediction of the remaining life of the rolling shaft, and has a high value for practical engineering applications.
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Index Terms
- Staged life prediction of rolling bearing based on improved GA_BP neural network
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Published In
September 2020
250 pages
ISBN:9781450387859
DOI:10.1145/3422713
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Association for Computing Machinery
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Published: 23 October 2020
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ICBDT 2020
ICBDT 2020: 2020 3rd International Conference on Big Data Technologies
September 18 - 20, 2020
Qingdao, China
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