|Table of Contents|

[1] Ding Yunhao, Jia Minping,. A multi-scale convolutional auto-encoder and its applicationin fault diagnosis of rolling bearings [J]. Journal of Southeast University (English Edition), 2019, 35 (4): 417-423. [doi:10.3969/j.issn.1003-7985.2019.04.003]

A multi-scale convolutional auto-encoder and its applicationin fault diagnosis of rolling bearings()

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

2019 4
Research Field:
Mechanical Engineering
Publishing date:


A multi-scale convolutional auto-encoder and its applicationin fault diagnosis of rolling bearings
Ding Yunhao Jia Minping
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
fault diagnosis deep learning convolutional auto-encoder multi-scale convolutional kernel feature extraction
Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery, a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed, based on the standard convolutional auto-encoder. In this model, the parallel convolutional and deconvolutional kernels of different scales are used to extract the features from the input signal and reconstruct the input signal; then the feature map extracted by multi-scale convolutional kernels is used as the input of the classifier; and finally the parameters of the whole model are fine-tuned using labeled data. Experiments on one set of simulation fault data and two sets of rolling bearing fault data are conducted to validate the proposed method. The results show that the model can achieve 99.75%, 99.3% and 100% diagnostic accuracy, respectively. In addition, the diagnostic accuracy and reconstruction error of the one-dimensional multi-scale convolutional auto-encoder are compared with traditional machine learning, convolutional neural networks and a traditional convolutional auto-encoder. The final results show that the proposed model has a better recognition effect for rolling bearing fault data.


[1] Li H, Xiao D Y. Survey on data driven fault diagnosis methods[J]. Control and Decision, 2011, 26(1): 1-9, 16. DOI:10.13195/j.cd.2011.01.3.lih.016. (in Chinese)
[2] Ren H, Qu J F, Chai Y. Research status and challenges of deep learning in fault diagnosis[J]. Control and Decision, 2017, 32(8):1345-1358.(in Chinese)
[3] Hinton G E. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. DOI:10.1126/science.1127647.
[4] Lei Y G, Jia F, Zhou X. Mechanical equipment big data health monitoring method based on deep learning theory[J]. Journal of Mechanical Engineering, 2015, 51(21):49-56.(in Chinese)
[5] She D M, Jia M P. Wear indicator construction of rolling bearings based on multi-channel deep convolutional neural network with exponentially decaying learning rate[J]. Measurement, 2019, 135: 368-375. DOI:10.1016/j.measurement.2018.11.040.
[6] Zhao R, Yan R Q, Chen Z H, et al. Deep learning and its applications to machine health monitoring[J]. Mechanical Systems and Signal Processing, 2019, 115: 213-237. DOI:10.1016/j.ymssp.2018.05.050.
[7] LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. DOI:10.1109/5.726791.
[8] Liu X C, Zhou Q C, Zhao J, et al. Real-time anti-noise fault diagnosis algorithm for one-dimensional convolutional neural networks[J]. Journal of Harbin Institute of Technology, 2019, 51(7):89-95.(in Chinese)
[9] Zhang W, Peng G L, Li C H. Bearings fault diagnosis based on convolutional neural networks with 2-D representation of vibration signals as input[J]. MATEC Web of Conferences, 2017, 95: 13001. DOI:10.1051/matecconf/20179513001.
[10] Masci J, Meier U, Cire瘙塂an D, et al. Stacked convolutional auto-encoders for hierarchical feature extraction[M]//Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011: 52-59. DOI:10.1007/978-3-642-21735-7_7.
[11] Li F F, Qiao H, Zhang B. Discriminatively boosted image clustering with fully convolutional auto-encoders[J]. Pattern Recognition, 2018, 83: 161-173. DOI:10.1016/j.patcog.2018.05.019.
[12] Liu X C, Zhou Q C, Zhao J, et al. Fault diagnosis of rotating machinery under noisy environment conditions based on a 1-D convolutional autoencoder and 1-D convolutional neural network[J]. Sensors, 2019, 19(4): 972-993. DOI:10.3390/s19040972.
[13] Zhang X N, Xiang Z, Tang C H. A deep convolutional autoencoder and its application in fault diagnosis of rolling bearings[J]. Journal of Xi’an Jiaotong University, 2018, 52(7):6-13, 64.(in Chinese)
[14] Kingma D P, Ba J. Adam: A method for stochastic optimization[J/OL].Computer Science, 2014. https://arxiv.org/pdf/1412.6980v8.pdf.
[15] Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533-536. DOI:10.1038/323533a0.
[16] Lei Y G, Jia F, Lin J, et al. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data[J]. IEEE Transactions on Industrial Electronics, 2016, 63(5): 3137-3147. DOI:10.1109/tie.2016.2519325.


Biographies: Ding Yunhao(1992—), 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: Ding Yunhao, Jia Minping. A multi-scale convolutional auto-encoder and its application in fault diagnosis of rolling bearings[J].Journal of Southeast University(English Edition), 2019, 35(4):417-423.DOI:10.3969/j.issn.1003-7985.2019.04.003.
Last Update: 2019-12-20