|Table of Contents|

[1] Yang Zhenbo, Jia Minping,. GA-1DLCNN method and its applicationin bearing fault diagnosis [J]. Journal of Southeast University (English Edition), 2019, 35 (1): 36-42. [doi:10.3969/j.issn.1003-7985.2019.01.006]

GA-1DLCNN method and its applicationin bearing fault diagnosis()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

2019 1
Research Field:
Mechanical Engineering
Publishing date:


GA-1DLCNN method and its applicationin bearing fault diagnosis
Yang Zhenbo Jia Minping
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
one-dimensional convolution neural network large-size convolution kernel hyper-parameter optimization genetic algorithm
Due to the fact that the vibration signal of the rotating machine is one-dimensional and the large-scale convolution kernel can obtain a better perception field, on the basis of the classical convolution neural network model(LetNet-5), one-dimensional large-kernel convolution neural network(1DLCNN)is designed. Since the hyper-parameters of 1DLCNN have a greater impact on network performance, the genetic algorithm(GA)is used to optimize the hyper-parameters, and the method of optimizing the parameters of 1DLCNN by the genetic algorithm is named GA-1DLCNN. The experimental results show that the optimal network model based on the GA-1DLCNN method can achieve 99.9% fault diagnosis accuracy, which is much higher than those of other traditional fault diagnosis methods. In addition, the 1DLCNN is compared with one-dimencional small-kernel convolution neural network(1DSCNN)and the classical two-dimensional convolution neural network model. The input sample lengths are set to be 128, 256, 512, 1 024, and 2 048, respectively, and the final diagnostic accuracy results and the visual scatter plot show that the effect of 1DLCNN is optimal.


[1] Wu Z Y, Yuan H Q. Fault diagnosis of an engine with an ant colony support vector machine [J]. Journal of Vibration and Shock, 2009, 28(3): 83-86, 201. DOI:10.13465/j.cnki.jvs.2009.03.037. (in Chinese)
[2] Hu Q, He Z J, Zhang Z S, et al. Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble[J].Mechanical Systems and Signal Processing, 2007, 21(2): 688-705. DOI:10.1016/j.ymssp.2006.01.007.
[3] Yuan S F, Chu F L. Support vector machines-based fault diagnosis for turbo-pump rotor[J].Mechanical Systems and Signal Processing, 2006, 20(4): 939-952. DOI:10.1016/j.ymssp.2005.09.006.
[4] Abbasion S, Rafsanjani A, Farshidianfar A, et al. Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine[J].Mechanical Systems and Signal Processing, 2007, 21(7): 2933-2945. DOI:10.1016/j.ymssp.2007.02.003.
[5] Han M H, Pan J L. A fault diagnosis method combined with LMD, sample entropy and energy ratio for roller bearings[J].Measurement, 2015, 76: 7-19. DOI:10.1016/j.measurement.2015.08.019.
[6] Wu G. Neural networks based fault diagnosis of rolling bearing [D]. Harbin: Harbin Institute of Technology, 2008.(in Chinese)
[7] You W, Shen C Q, Guo X J, et al. Bearing fault diagnosis using convolution neural network and support vector regression[C]// The 2017 International Conference on Mechanical Engineering and Control Automation. Nanjing, China, 2017: 6-11. DOI:10.12783/dtetr/icmeca2017/11904.
[8] Ding X X, He Q B. Energy-fluctuated multiscale feature learning with deep ConvNet for intelligent spindle bearing fault diagnosis[J].IEEE Transactions on Instrumentation and Measurement, 2017, 66(8): 1926-1935. DOI:10.1109/tim.2017.2674738.
[9] Chen Z Q, Li C, Sanchez R V. Gearbox fault identification and classification with convolutional neural networks[J].Shock and Vibration, 2015, 2015: 1-10. DOI:10.1155/2015/390134.
[10] Guo X J, Chen L, Shen C Q. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis[J].Measurement, 2016, 93: 490-502. DOI:10.1016/j.measurement.2016.07.054.
[11] Janssens O, Slavkovikj V, Vervisch B, et al. Convolutional neural network based fault detection for rotating machinery[J].Journal of Sound and Vibration, 2016, 377: 331-345. DOI:10.1016/j.jsv.2016.05.027.
[12] Wang F A, Jiang H K, Shao H D, et al. An adaptive deep convolutional neural network for rolling bearing fault diagnosis[J].Measurement Science and Technology, 2017, 28(9): 095005. DOI:10.1088/1361-6501/aa6e22.
[13] Wang X C, Shi F, Yu L, et al. 43 case studies of MATLAB neural network[M]. Beijing: Beihang University Press, 2013:21-23.(in Chinese)
[14] Fu D P, Zhai Y, Yu Q M. Study on fault diagnosis of rolling bearing based on EMD and support vector machine[J].Machine Tool & Hydraulics, 2017, 45(11): 184-187. DOI:10.3969/j.issn.1001-3881.2017.11.042. (in Chinese)
[15] Ye R Z, Li W H. Fault diagnosis for bearings based on wavelet packet decomposition and BP neural network[J]. Bearing, 2012(10): 53-56. DOI:10.3969/j.issn.1000-3762.2012.10.019. (in Chinese)
[16] Pan Z R, Liu X, Qiao Z J. Intelligent fault diagnosis of rolling bearing based on wavelet packet energy and BP neural network [J]. Automation & Instrumentation, 2015(5): 82-84. DOI:10.14016/j.cnki.1001-9227.2015.05.082. (in Chinese)
[17] Maaten L, Hinton G E. Visualizing data using t-SNE[J]. J Mach Learn Res, 2008, 9(3): 2579–2605.


Biographies: Yang Zhenbo(1995—), male, graduate; Jia Minping(corresponding author), male, doctor, professor, mpjia@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No.51675098).
Citation: Yang Zhenbo, Jia Minping.GA-1DLCNN method and its application in bearing fault diagnosis[J].Journal of Southeast University(English Edition), 2019, 35(1):36-42.DOI:10.3969/j.issn.1003-7985.2019.01.006.
Last Update: 2019-03-20