Slow feature‐constrained decomposition autoencoder: Application to process anomaly detection and localization

Summary Detecting anomalies in manufacturing processes is crucial for ensuring safety. However, noise significantly undermines the reliability of data‐driven anomaly detection models. To address this challenge, we propose a slow feature‐constrained decomposition autoencoder (SFC‐DAE) for anomaly det...

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Bibliographic Details

Published in:	International journal of adaptive control and signal processing Vol. 39; no. 7; pp. 1483 - 1502
Main Authors:	Jia, Mingwei, Jiang, Lingwei, Hu, Junhao, Liu, Yi, Chen, Tao
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 01.07.2025 Wiley Subscription Services, Inc
Subjects:	Anomalies anomaly detection Constraints Decomposition masked autoencoder noisy data process industry Sugar industry time series decomposition Trends Water treatment
ISSN:	0890-6327, 1099-1115
Online Access:	Get full text
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