Application of Deep Neural Network with Sparse Auto-encoder in Rolling Bearing Fault Diagnosis

Tang Fang; Liu Yilun; Long Hui

doi:10.13433/j.cnki.1003-8728.2018.0304

Volume 37 Issue 3

Mar. 2018

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2018 > 37(3): 352-357

Tang Fang, Liu Yilun, Long Hui. Application of Deep Neural Network with Sparse Auto-encoder in Rolling Bearing Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(3): 352-357. doi: 10.13433/j.cnki.1003-8728.2018.0304

Citation:

Tang Fang, Liu Yilun, Long Hui. Application of Deep Neural Network with Sparse Auto-encoder in Rolling Bearing Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(3): 352-357. doi: 10.13433/j.cnki.1003-8728.2018.0304

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Application of Deep Neural Network with Sparse Auto-encoder in Rolling Bearing Fault Diagnosis

doi: 10.13433/j.cnki.1003-8728.2018.0304

Tang Fang¹,
Liu Yilun^{1,2
,
,},
Long Hui¹

1.
College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China;
2.
Light Alloy Research Institute, Central South University, Changsha 410083, China

Received Date: 2016-12-13
Publish Date: 2018-03-05

Abstract

Abstract

To overcome the problem of using supervised learning to extract fault features for most current rolling bearing fault diagnosis methods, a deep neural network algorithm is proposed, which is realized sparse auto-encoder, to achieve unsupervised feature learning by automatic extracting the inherent characteristics of the rolling bearing vibration signal for fault diagnosis of rolling bearing fault diagnosis. Firstly, the spectrum of the bearing vibration signal is used to train sparse auto-encoder in order to obtain parameters; secondly, the parameters from sparse auto-encoder and spectrum of the rolling bearing vibration signal are used to train the deep neural network, and the back-propagation algorithm is used for fine-tuning the deep neural network with the purpose of improving classification accuracy. Finally, the deep neural network has been trained to identify faults of rolling bearings. The analysis results from vibration signals with roller normal condition of the rolling bearing,pitting fault of bearing outer ring, pitting fault of bearing inner ring and crack fault of bearing rolling element show that, compared with back propagation neural network, the proposed deep neural network can accurately identify fault type of rolling bearing faults.
- sparse auto-encoder,
- deep neural network,
- rolling bearing,
- fault diagnosis

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References(22)

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