Application of stack marginalised sparse denoising auto-encoder in fault diagnosis of rolling bearing

When a fracturing vehicle is working, it generally needs to bear high loads, media corrosion and erosion. For this special working environment, this study proposes a rolling bearing fault diagnosis method based on stack marginalised sparse denoising auto-encoder (SDAE). This method combines the spar...

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Bibliographic Details
Published in:Journal of engineering (Stevenage, England) Vol. 2018; no. 16; pp. 1772 - 1777
Main Authors: Zhang, Junling, Chen, Zhigang, Du, Xiaolei, Xu, Xu, Yu, Miao
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 01.11.2018
Subjects:
classification testing
computational complexity
corrosion
cracking
cracks
DAE
denoising autoencoder
erosion
failure analysis
fault diagnosis
fracture
fracturing vehicle
learning training
mechanical engineering computing
media corrosion
pitting corrosion failure
rolling bearing fault diagnosis method
rolling bearings
SAE
SDAE
signal denoising
softmax classifier
sparse autoencoder
stack marginalised sparse denoising autoencoder application
The 2nd 2018 Asian Conference on Artificial Intelligence Technology (ACAIT 2018)
two‐layer stacking method
fault diagnosis
corrosion
stack marginalised sparse denoising autoencoder application
two-layer stacking method
failure analysis
fracture
signal denoising
SDAE
pitting corrosion failure
cracks
softmax classifier
fracturing vehicle
sparse autoencoder
DAE
classification testing
denoising autoencoder
learning training
cracking
rolling bearing fault diagnosis method
rolling bearings
mechanical engineering computing
media corrosion
SAE
erosion
computational complexity
ISSN:2051-3305, 2051-3305
Online Access:Get full text
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Summary:When a fracturing vehicle is working, it generally needs to bear high loads, media corrosion and erosion. For this special working environment, this study proposes a rolling bearing fault diagnosis method based on stack marginalised sparse denoising auto-encoder (SDAE). This method combines the sparse auto-encoder (SAE) and the denoising auto-encoder (DAE) and combines the characteristics of dimensionality reduction and robustness. The method adds marginalisation to optimise the SDAE. Finally, it uses a two-layer stacking method. The output results of the second marginalised SDAE are used as input to the softmax classifier for learning training and classification testing. This improved method (stack SDAE) improves the denoising ability, reduces the computational complexity, solves the problems of difficult parameter adjustment and slows training convergence. The experimental tests were carried out on the failure of pitting corrosion of the outer ring of the bearing, pitting failure of the inner ring, and cracking of the rolling element. The results show that the algorithm can effectively improve the accuracy of fault diagnosis of rolling bearings, and it has greatly improved than the algorithms of SAEs and DAE.
ISSN:2051-3305
2051-3305
DOI:10.1049/joe.2018.8267