A Novel Fatigue Reliability Calculation Method Based on INGO-BPNN

doi:10.23940/ijpe.24.05.p7.319331

Abstract

Abstract: A novel method is proposed to augment the precision and efficiency of the fatigue reliability analysis. This method utilizes an Improved Northern Goshawk Optimization (INGO) algorithm to optimize the BP neural network (BPNN). The enhanced Circle Chaotic Map, adaptive inertia weight, Elite opposition-based learning strategy, and artificial rabbits optimization strategy are incorporated to enhance both the accuracy and speed of the optimization algorithm. Proposing a fatigue reliability calculation approach for the bogie frame based on the maximum material utilization rate. The approach computes fatigue reliability through the segmentation of all bogie frame welds, employing the maximum material utilization rate within each experimental design scheme. The INGO-BPNN is trained using 80 sample sets acquired through experimental design. The fatigue reliability of the bogie frame is assessed using the Monte Carlo method based on the surrogate model. Research findings indicate that the surrogate model achieves a predictive accuracy of 99.8%, while the fatigue reliability of the bogie frame stands at 99.36%. The proposed method improves the calculation efficiency while ensuring the prediction accuracy of the model.

Key words: bogie frame, fatigue reliability, BP neural network, INGO algorithm

Kangjun Xu, Yonghua Li, Qi Gong, Dongxu Zhang, and Tao Guo. A Novel Fatigue Reliability Calculation Method Based on INGO-BPNN [J]. Int J Performability Eng, 2024, 20(5): 319-332.

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