Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder

Condition monitoring and fault diagnosis are important for maintaining the system performance and guaranteeing the operational safety. The traditional data-driven approaches mostly incorporate well-defined features and methodologies such as supervised artificial intelligence algorithms. Prior knowle...

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Veröffentlicht in:	IET science, measurement & technology Jg. 11; H. 6; S. 687 - 695
Hauptverfasser:	Xia, Min, Li, Teng, Liu, Lizhi, Xu, Lin, de Silva, Clarence W
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 01.09.2017
Schlagworte:	condition monitoring data‐driven approach deep neural network encoding fault classification fault diagnosis feature extraction intelligent fault diagnosis approach neural nets operational safety representative feature extraction Research Article signal classification signal denoising signal representation stacked denoising autoencoder supervised artificial intelligence algorithms trained DNN model unsupervised feature learning unsupervised learning stacked denoising autoencoder fault diagnosis supervised artificial intelligence algorithms intelligent fault diagnosis approach operational safety encoding unsupervised feature learning representative feature extraction signal representation unsupervised learning signal classification signal denoising fault classification feature extraction data-driven approach trained DNN model condition monitoring neural nets deep neural network
ISSN:	1751-8822, 1751-8830
Online-Zugang:	Volltext
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