One-dimensional decoupled convolutional autoencoder with sparse self-attention mechanism for process monitoring

Industrial processes are constantly disturbed by environmental and human factors during operation. Undifferentiated alarming of these disturbances will bring serious alarm disaster problems.Effectively distinguishing the disturbances that have different effects on the process operation state can hel...

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Veröffentlicht in:	Process safety and environmental protection Jg. 199; S. 107156
Hauptverfasser:	Yang, Yuguo, Shi, Hongbo, Song, Bing, Tao, Yang, Guo, Lei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.07.2025
Schlagworte:	Convolutional autoencoder Data distribution alignment Process knowledge embedding Process monitoring Variable-level feature extraction and interaction Process monitoring Process knowledge embedding Data distribution alignment Convolutional autoencoder Variable-level feature extraction and interaction
ISSN:	0957-5820
Online-Zugang:	Volltext
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