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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Vydané v:	International journal of adaptive control and signal processing Ročník 39; číslo 7; s. 1483 - 1502
Hlavní autori:	Jia, Mingwei, Jiang, Lingwei, Hu, Junhao, Liu, Yi, Chen, Tao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken, USA John Wiley & Sons, Inc 01.07.2025 Wiley Subscription Services, Inc
Predmet:	Anomalies anomaly detection Constraints Decomposition masked autoencoder noisy data process industry Sugar industry time series decomposition Trends Water treatment
ISSN:	0890-6327, 1099-1115
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