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Improved shuffled frog leaping algorithm-based BP neural network and its application in bearing early fault diagnosis

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Abstract

This paper reports on a new back propagation (BP) neural network based on an improved shuffled frog leaping algorithm (ISFLA) and its application in bearing fault diagnosis. The ISFLA is developed on the basis of a chaotic operator and the convergence factor of particle swarm optimization to overcome the shortcomings of conventional shuffled frog leaping algorithm (SFLA). Testing results show that the proposed algorithm can effectively improve the solution accuracy and convergence properties and exhibits an excellent ability of global optimization in high-dimensional space. The presented ISFLA is then employed to optimize the weights and threshold values of BP neural network. An ISFLA-BP network model is established for the early fault diagnosis of rolling bearings. The proposed ISFLA-BP scheme has been compared with BP and SFLA-BP networks through experimental studies. Results indicate that the developed new model demonstrates better generalization capability and stronger robustness. It is able to effectively improve the efficiency of network training and the accuracy of early fault pattern recognition in bearing fault diagnosis tasks.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 51075070, in part by the Jiangsu Province Research Innovation Program for College Graduates, China, under Grant CXZZ_0139, in part by the Anhui Province Foundation for Youth Scholars of Educational Commission, China, under Grant 2012SQRL085, in part by Anhui Province Natural Science Foundation of China under Grant 1308085ME78, and in part by the Macao Science and Technology Development Fund under Grant 070/2012/A3.

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Authors and Affiliations

  1. School of Mechanical Engineering, Southeast University, Nanjing, 211189, China

    Zhuanzhe Zhao & Minping Jia

  2. School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu, 241000, China

    Zhuanzhe Zhao

  3. Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Avenida da Universidade, Taipa, Macau, China

    Qingsong Xu

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  1. Zhuanzhe Zhao
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  3. Minping Jia
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Correspondence to Minping Jia.

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Zhao, Z., Xu, Q. & Jia, M. Improved shuffled frog leaping algorithm-based BP neural network and its application in bearing early fault diagnosis. Neural Comput & Applic 27, 375–385 (2016). https://doi.org/10.1007/s00521-015-1850-y

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  • DOI: https://doi.org/10.1007/s00521-015-1850-y

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