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

Author:

Jia Mingwei1ORCID,Jiang Lingwei2,Hu Junhao1,Liu Yi1,Chen Tao2

Affiliation:

1. Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou People's Republic of China

2. School of Chemistry and Chemical Engineering University of Surrey Guildford UK

Abstract

SummaryDetecting 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.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Provincial Universities of Zhejiang

Publisher

Wiley

Reference36 articles.

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