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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