Gearbox fault diagnosis based on feature learning of multi-channel one-dimensional convolutional neural network

被引：0
|
作者：
Ye Z. [1 ]
Yu J. [1 ]
机构：
[1] School of Mechanical Engineering, Tongji University, Shanghai
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2020年 / 39卷 / 20期
关键词：
Convolutional neural network; Feature learning; Gearbox fault diagnosis; Multi-channel signal; Stacked denoised autoencoder;
D O I：
10.13465/j.cnki.jvs.2020.20.008
中图分类号：
学科分类号：
摘要：
A new DNN model, called multi-channel one-dimensional convolutional neural network (MC-1DCNN) was proposed in order to solve the problem of using single-channel signal images as input, which can not express the fault characteristics hidden in the vibration signals effectively. Firstly, empirical mode decomposition (EMD) was used to obtain the multi-channel one-dimensional signals. Secondly, MC-1DCNN was constructed to perform the feature extraction of the multi-channel one-dimensional signals. Finally, stacked denoised autoencoder (SDAE) was embedded after the fully connected layer for further feature extraction and classification. The effectiveness of the proposed method was verified on the gearbox test rig. The experimental results show that the proposed method has better performance on feature extraction and fault diagnosis than typical DNNs and other regular classifiers. © 2020, Editorial Office of Journal of Vibration and Shock. All right reserved.
引用
收藏
页码：55 / 66
页数：11
相关论文
共 24 条
  • [1] JIAO J, ZHAO M, LIN J, Et al., A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes, Knowledge-Based Systems, 160, pp. 237-250, (2018)
  • [2] SUN R, YANG Z, CHEN X, Et al., Gear fault diagnosis based on the structured sparsity time-frequency analysis, Mechanical Systems and Signal Processing, 102, pp. 346-363, (2018)
  • [3] LI Heng, ZHANG Qing, QIN Xianrong, Et al., Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network, Journal of Vibration and Shock, 37, 19, pp. 124-131, (2018)
  • [4] LIU H, ZHANG J, CHENG Y, Et al., Fault diagnosis of gearbox using empirical mode decomposition and multi-fractal detrended cross-correlation analysis, Journal of Sound and Vibration, 385, pp. 350-371, (2016)
  • [5] VINCENT P, LAROCHELLE H, LAJOIE I, Et al., Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Journal of Machine Learning Research, 11, pp. 3371-3408, (2010)
  • [6] KRIZHEVSKY A, SUTSKEVER I, HINTON G., ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, 25, 2, pp. 1-9, (2012)
  • [7] ZHAO D, WANG T, CHU F., Deep convolutional neural network based planet bearing fault classification, Computers in Industry, 107, pp. 59-66, (2019)
  • [8] YU J., A selective deep stacked denoising autoencoders ensemble with negative correlation learning for gearbox fault diagnosis, Computers in Industry, 108, pp. 62-72, (2019)
  • [9] PENG B, XIA H, LIU X, Et al., Research on intelligent fault diagnosis method for nuclear power plant based on correlation analysis and deep belief network, Progress in Nuclear Energy, 108, pp. 419-427, (2018)
  • [10] CHEN Baojia, LIU Haotao, XU Chao, Et al., Gear fault diagnosis based on DBNS, China Mechanical Engineering, 30, 2, pp. 205-211, (2019)
123