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Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm

Author

Listed:
  • Yu Zhang
  • Jiawen Zhang
  • Lin Luo
  • Xiaorong Gao

Abstract

It is beneficial for maintenance department to make maintenance strategy and reduce maintenance cost to forecast the hidden danger index value. Based on the analysis of the research status of wheel-to-life prediction at home and abroad and the repair of wheel-set wear and tear, this article designs and implements an adaptive differential evolution algorithm Levenberg–Marquardt back propagation wheel-set size prediction model. Aiming at the shortcomings of back propagation neural network, it is easy to fall into local extreme value. The back propagation algorithm is improved by Levenberg–Marquardt numerical optimization algorithm. Aiming at the shortcomings of back propagation neural network algorithm for randomly initializing connection weights and thresholds to fall into local extreme value, the differential evolution algorithm is used to optimize the initial connection weights and thresholds between the layers of the neural network. In order to speed up the search of the optimal initial weights and thresholds of the differential evolution algorithm Levenberg–Marquardt back propagation neural network, the initial values are further optimized, and an adaptive differential evolution algorithm Levenberg–Marquardt back propagation wheel-set size prediction model is designed and implemented. Compared with the proposed combine adaptive differential evolution algorithm with LMBP optimization (ADE-LMBP) is effective and significantly improves the prediction accuracy.

Suggested Citation

  • Yu Zhang & Jiawen Zhang & Lin Luo & Xiaorong Gao, 2019. "Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:10:p:1550147719881348
    DOI: 10.1177/1550147719881348
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    References listed on IDEAS

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    1. Lin, Jing & Pulido, Julio & Asplund, Matthias, 2015. "Reliability analysis for preventive maintenance based on classical and Bayesian semi-parametric degradation approaches using locomotive wheel-sets as a case study," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 143-156.
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    Cited by:

    1. Francesc Pozo & Diego A Tibaduiza & Miguel à ngel Torres-Arredondo & Margarita Varón & Hernán Dario Ceron-Muñoz, 2020. "Editorial," International Journal of Distributed Sensor Networks, , vol. 16(9), pages 15501477209, September.
    2. Guoqing An & Ziyao Jiang & Libo Chen & Xin Cao & Zheng Li & Yuyang Zhao & Hexu Sun, 2021. "Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine," Sustainability, MDPI, vol. 13(18), pages 1-18, September.

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