A mechanical fault diagnosis model with semi-supervised variational autoencoder based on long short-term memory network

Condition monitoring and accurate fault diagnosis are always concerned for stable operating of mechanical equipment. The fault diagnosis based on supervised deep learning has been proved to be effective by their powerful capacities in feature extracting, but usually requiring large number of labeled...

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Veröffentlicht in:	Nonlinear dynamics Jg. 113; H. 1; S. 459 - 478
Hauptverfasser:	Qu, Yuanyuan, Li, Tao, Fu, Shichen, Wang, Zhisheng, Chen, Jian, Zhang, Yupeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.01.2025 Springer Nature B.V
Schlagworte:	Accuracy Applications of Nonlinear Dynamics and Chaos Theory Classical Mechanics Cluster analysis Clustering Coal mining Condition monitoring Control Deep learning Dynamical Systems Fault diagnosis Feature extraction Machine learning Original Paper Physics Physics and Astronomy Regularization Semi-supervised learning Statistical Physics and Dynamical Systems Vibration Vibration analysis Fault diagnosis Variational autoencoder Long short-term memory network Clustering analysis Semi-supervised learning
ISSN:	0924-090X, 1573-269X
Online-Zugang:	Volltext
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