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| Fatigue reliability analysis of bogie frame based on active learning neural network |
| Received:November 16, 2021 Revised:January 27, 2022 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx20211116002 |
| KeyWord:structural reliability BP neural network sequence sampling bogie frame fatigue reliability analysis |
| Author | Institution |
| 陈鹏 |
大连交通大学 车辆工程学院, 大连 |
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| Abstract: |
| An active learning BR-BP neural network combining Monte Carlo simulation method is proposed to improve the accuracy and efficiency of fatigue reliability analysis of a bogie frame.In the method, the prediction accuracy and convergence speed of the neural network are improved by using Bayesian Regularization training algorithm.Considering the inherent characteristics of reliability analysis, an active learning function that can be suitable for BP neural network is constructed to guide the selection of optimal sample points.Through the learning function, these samples are guaranteed to be distributed near the limit state function, and consider the contribution of prediction error and the distribution of sample points to the failure probability.The reliability analysis results of the bogie frame show that the prediction accuracy and calculation efficiency can be satisfied at the same time, which verifies the superiority and feasibility of the proposed method. |