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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.

References

  1. Ali JB, Fnaiech N, Saidi L et al (2015) Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl Acoust 89:16–27

    Article  Google Scholar 

  2. Ali A, Zhu Y, Chen Q, et al (2019) Leveraging spatio-temporal patterns for predicting citywide traffic crowd flows using deep hybrid neural networks. In: IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2019, pp 125–132

  3. Ali A, Zhu Y, Zakarya M (2021) A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing. Multimed Tools Appl 1–33

  4. Boying Z, Peng X, Li Z (2019) Job scheduling algorithm based on hybrid parallel cuckoo search. Comput Eng Des 40(03):719–724

    Google Scholar 

  5. Cai C, Qian Q, Fu Y (2020) Application of BAS-Elman neural network in prediction of blasting vibration velocity. Procedia Comput Sci 166:491–495

    Article  Google Scholar 

  6. Case Western Reserve University Bearing Data Center. Download a Data File[DB/OL]. [2020–12–18]. http://www.eecs.case.edu/laboratory/bearing/download.htm. Accessed 8 Dec 2020

  7. Changqing S, Yumei Qi, Jun W et al (2018) An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder. Eng Appl Artif Intell 76:170–184

    Article  Google Scholar 

  8. Chen Z, Gryllias K, Li W (2019) Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine. Mech Syst Signal Process 33:106272

    Article  Google Scholar 

  9. Elfahham Y (2019) Estimation and prediction of construction cost index using neural networks, time series, and regression. Alex Eng J 58(2):499–506

    Article  Google Scholar 

  10. ElSaid A, El Jamiy F, Higgins J et al (2018) Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration. Appl Soft Comput 73:969–991

    Article  Google Scholar 

  11. Hemanth DJ, Anitha J, Son LH (2018) Brain signal based human emotion analysis by circular back propagation and Deep Kohonen Neural Networks. Comput Electr Eng 68:170–180

    Article  Google Scholar 

  12. Ismail A, Jeng D-S, Zhang LL (2013) An optimised product-unit neural network with a novel PSO-BP hybrid training algorithm: applications to load–deformation analysis of axially loaded piles. Eng Appl Artif Intell 26(10):2305–2314

    Article  Google Scholar 

  13. Jarusek R, Volna E, Kotyrba M (2019) Photomontage detection using steganography technique based on a neural network. Neural Netw 116:150–165

    Article  Google Scholar 

  14. Lei H, Rui Li, Huili Z (2011) Comprehensive evaluation model of soil nutrient based on BP neural network. Trans Chin Soc Agric Mach 42(07):109–115

    Google Scholar 

  15. Lili T, Fuqi Lv (2015) Fault diagnosis for rolling bearing based on BP neural network of genetic algorithm. Mach Des Manuf Eng 44(03):65–68

    Google Scholar 

  16. Ouaarab A, Ahiod B, Yang X (2014) Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput Appl 24(7/8):1659–1669

    Article  Google Scholar 

  17. Pengyu C, Zeyong W, Chunrong Q et al (2019) Fault diagnosis of rolling bearings based on IBA optimized BP neural network. Electron Meas Technol 42(12):33–36

    Google Scholar 

  18. Rajakarunakaran S, Venkumar P, Devaraj D et al (2008) Artificial neural network approach for fault detection in rotary system. Appl Soft Comput 8(1):740–748

    Article  Google Scholar 

  19. Sasikala J, Juliet DS (2019) Optimized vessel detection in marine environment using hybrid adaptive cuckoo search algorithm. Comput Electr Eng 78:482–492

    Article  Google Scholar 

  20. Shengxue T, Hongjun C, Zhigang L (2015) Fault diagnosis fusion method for analog circuits based on wavelet and neural network. J Central S Univ (Sci Technol) 46(01):127–134

    Google Scholar 

  21. Shuwei W (2018) Application of wavelet transform in fault signal analysis of rolling bearings. J Shanxi Datong Univ 1:69

    Google Scholar 

  22. Tyagi S, Panigrahi SK (2017) An improved envelope detection method using particle swarm optimisation for rolling element bearing fault diagnosis. J Comput Des Eng 4(4):305–317

    Google Scholar 

  23. Wang S, Zhang N, Wu L et al (2016) Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. Renew Energy 94:629–636

    Article  Google Scholar 

  24. Xiaomei Xu, Lei Z, Yiping J et al (2019) Active control on path following and lateral stability for truck–trailer combinations. Arab J Sci Eng 44(2):1365–1377

    Article  Google Scholar 

  25. Xing YF, Wang YS, Shi L et al (2016) Sound quality recognition using optimal wavelet-packet transform and artificial neural network methods. Mech Syst Signal Process 66–67:875–892

    Article  Google Scholar 

  26. Xingxing J, Changqing S, Juanjuan S et al (2018) Initial center frequency-guided VMD for fault diagnosis of rotating machines. J Sound Vib 435:36–55

    Article  Google Scholar 

  27. Yan X, Jia M (2018) A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing. Neurocomputing 313:47–64

    Article  Google Scholar 

  28. Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. J Math Model Numer Optim 1(4):330–343

    MATH  Google Scholar 

  29. Yang J, Tang Z, Wang X et al (2019) Roadheader anomaly detection method based on VSAPSO-BP under the single category learning. J Vib Meas Diagn 39(01):130–135

    Google Scholar 

  30. Yi JH, Xu WH, Chen YT (2014) Novel back propagation optimization by cuckoo search algorithm. Sci World J 1:878262

    Google Scholar 

  31. Yu J (2012) Health condition monitoring of machines based on Hidden Markov Model and contribution analysis. IEEE Trans Instrum Meas 61(8):2200

    Article  Google Scholar 

  32. Zhang Z, Shifei D, Weikuan J (2019) A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems. Eng Appl Artif Intell 85:254–268

    Article  Google Scholar 

  33. Zhang Z, Ding S, Jia W (2019) A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems. Eng Appl Artif Intell 85:254–268

    Article  Google Scholar 

  34. Zhenyou Z, Yi W, Kesheng W (2013) Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network. J Intell Manuf 24(6):1213–1227

    Article  Google Scholar 

  35. Zhiqiang S, Geng D, Su C et al (2017) Vibration prediction of a hydro-power house base on IFA-BPNN. J Vib Shock 36(24):64–69

    Google Scholar 

Download references

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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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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  • DOI: https://doi.org/10.1007/s11042-021-11556-x

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