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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 detection in noisy scenarios. Considering that the process can exhibit both long‐term trends and periodic properties, the process data is decomposed into trends and cycles. The repetitive information is mitigated by slicing and randomly masking certain trends and cycles. Dependencies among slices are constructed to extract intrinsic information, while high‐frequency noise is reduced using a slow feature‐constrained loss. Anomalies are detected and localized through a reconstruction error strategy. The effectiveness of SFC‐DAE is demonstrated using data from a sugar factory and a secure water treatment system.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0890-6327
1099-1115
DOI:10.1002/acs.3888