Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common...

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Vydáno v:	Entropy (Basel, Switzerland) Ročník 24; číslo 1; s. 36
Hlavní autoři:	Yan, Xiaoan, Xu, Yadong, She, Daoming, Zhang, Wan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 24.12.2021 MDPI
Témata:	Accuracy Algorithms Background noise Bearings Coders Deep learning Fault diagnosis Feature extraction Heuristic methods Machine learning Mathematical models Neural networks Noise Noise reduction Optimization Optimization algorithms Parameters Robustness Roller bearings rolling bearing seagull optimization algorithm stacked variational denoising auto-encoder Support vector machines Teaching methods Vibration analysis Wavelet transforms rolling bearing fault diagnosis seagull optimization algorithm stacked variational denoising auto-encoder
ISSN:	1099-4300, 1099-4300
On-line přístup:	Získat plný text
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