Slow feature‐constrained decomposition autoencoder: Application to process anomaly detection and localization
Gespeichert in:
| Titel: | Slow feature‐constrained decomposition autoencoder: Application to process anomaly detection and localization |
|---|---|
| Autoren: | Mingwei Jia, Lingwei Jiang, Junhao Hu, Yi Liu, Tao Chen |
| Quelle: | International Journal of Adaptive Control and Signal Processing. 39:1483-1502 |
| Verlagsinformationen: | Wiley, 2024. |
| Publikationsjahr: | 2024 |
| Schlagwörter: | 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 6. Clean water |
| Beschreibung: | 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. |
| Publikationsart: | Article |
| Sprache: | English |
| ISSN: | 1099-1115 0890-6327 |
| DOI: | 10.1002/acs.3888 |
| Rights: | Wiley Online Library User Agreement |
| Dokumentencode: | edsair.doi...........a10342458b4efaf8a124da5f65e97e56 |
| Datenbank: | OpenAIRE |
Full Text Finder 
Nájsť tento článok vo Web of Science | 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. |
|---|---|
| ISSN: | 10991115 08906327 |
| DOI: | 10.1002/acs.3888 |