Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder

Intelligent fault diagnosis algorithm for rolling bearings has received increasing attention. However, in actual industrial environments, most rolling bearings work under severe working conditions of variable speed and strong noise, which makes the performance of many intelligent fault diagnosis met...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 20; číslo 20; s. 5734
Hlavní autori:	Shi, Hongmei, Chen, Jingcheng, Si, Jin, Zheng, Changchang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 09.10.2020 MDPI
Predmet:	Accuracy Algorithms Artificial intelligence Bearings Decomposition Deep learning dilated pyramid network Fault diagnosis fully convolutional denoising autoencoder intelligent fault diagnosis Machine learning Machinery Methods Neural networks Noise noise robustness residual learning Signal processing Support vector machines Wavelet transforms
ISSN:	1424-8220, 1424-8220
On-line prístup:	Získať plný text
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