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Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm

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Abstract

In order to improve the accuracy of rolling bearing fault diagnosis in mechanical equipment, a new fault diagnosis method based on back propagation neural network optimized by cuckoo search algorithm is proposed. This method use the global search ability of the cuckoo search algorithm to constantly search for the best weights and thresholds, and then give it to the back propagation neural network. In this paper, wavelet packet decomposition is used for feature extraction of vibration signals. The energy values of different frequency bands are obtained through wavelet packet decomposition, and they are input as feature vectors into optimized back propagation neural network to identify different fault types of rolling bearings. Through the three sets of simulation comparison experiments of Matlab, the experimental results show that, Under the same conditions, compared with the other five models, the proposed back propagation neural network optimized by cuckoo search algorithm has the least number of training iterations and the highest diagnostic accuracy rate. And in the complex classification experiment with the same fault location but different bearing diameters, the fault recognition correct rate of the back propagation neural network optimized by cuckoo search algorithm is 96.25%.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The research is funded partially by the Jiangsu International Science and Technology Cooperation Project (BZ2021022) , the Key Research and Development Program of Jiangsu Province (BE2021362) and Program for Student Innovation through Research and Training of Nanjing Agricultural University (201910307200P).

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Authors and Affiliations

  1. College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China

    Maohua Xiao, Yabing Liao, Guosheng Geng & Ziwei Jiang

  2. Faculty of Agriculture, University of South Bohemia, 1668, Studentska, Czechia

    Petr Bartos & Martin Filip

Authors
  1. Maohua Xiao
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  2. Yabing Liao
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  3. Petr Bartos
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  4. Martin Filip
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  5. Guosheng Geng
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  6. Ziwei Jiang
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Correspondence to Maohua Xiao.

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Xiao, M., Liao, Y., Bartos, P. et al. Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm. Multimed Tools Appl 81, 1567–1587 (2022). https://doi.org/10.1007/s11042-021-11556-x

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