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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Vydáno v:	Journal of engineering (Stevenage, England) Ročník 2018; číslo 16; s. 1772 - 1777
Hlavní autoři:	Zhang, Junling, Chen, Zhigang, Du, Xiaolei, Xu, Xu, Yu, Miao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 01.11.2018
Témata:	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
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Popis
Shrnutí:	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