Convolutional Long Short-Term Memory Autoencoder-Based Feature Learning for Fault Detection in Industrial Processes

Process signals show the characteristics of large scale, high dimension, and strong correlation in modern industrial processes, which brings a big challenge for process fault detection and diagnosis. Due to the powerful feature learning ability, deep learning has been widely used in image and visual...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 70; S. 1 - 15
Hauptverfasser:	Yu, Jianbo, Liu, Xing, Ye, Lyujiangnan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Autoencoder Continuously stirred tank reactors Convolution Data mining deep learning Fault detection Fault diagnosis Feature extraction industrial process Logic gates long short-term memory Machine learning Principal component analysis Process monitoring Signal processing
ISSN:	0018-9456, 1557-9662
Online-Zugang:	Volltext
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