Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample

Massive volumes of data are needed for deep learning (DL) models to provide accurate diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the effectiveness of DL models over shallow machine learning (SL) in terms of feature extraction, feature dimensional reduction and di...

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Vydané v:	IEEE transactions on industrial informatics Ročník 16; číslo 10; s. 6263 - 6271
Hlavní autori:	Saufi, Syahril Ramadhan, Ahmad, Zair Asrar Bin, Leong, Mohd Salman, Lim, Meng Hee
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.10.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Analytical models Artificial neural networks Data models Datasets Deep learning Diagnostic systems Fault diagnosis Feature extraction gearbox Gearboxes image recognition limited data sample Machine learning Neural networks Object recognition Optimization Pattern recognition stacked sparse autoencoder (SSAE) System effectiveness Training Wind turbines
ISSN:	1551-3203, 1941-0050
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