Spatio-Temporal Attention Adversarial Autoencoders for Enhanced Anomaly Detection in High-Pressure Grinding Rolls

Consistent product quality and efficient operations in high-pressure grinding roll (HPGR) rely heavily on real-time anomaly detection. Complexities arise from fluctuations in raw materials, feeding processes, and unforeseen disruptions, along with the inherent spatio-temporal dynamics of sensor data...

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Vydané v:IEEE transactions on industrial informatics Ročník 21; číslo 4; s. 2917 - 2926
Hlavní autori: Zhang, Danwei, Yu, Wen, Xu, Quan, Chai, Tianyou
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.04.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adversarial autoencoder (AAE)
Algorithms
Anomalies
Anomaly detection
anomaly monitoring
Data models
end-edge-cloud collaborative architecture
Energy flow
Feature extraction
Grinding
High pressure
Logic gates
minimal gated unit (MGU)
Monitoring
Process variables
Production
Raw materials
Real time
Real-time systems
Root cause analysis
spatio-temporal attention (STA)
Spatiotemporal data
Time series analysis
Training
Vectors
ISSN:1551-3203, 1941-0050
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Popis
Shrnutí:Consistent product quality and efficient operations in high-pressure grinding roll (HPGR) rely heavily on real-time anomaly detection. Complexities arise from fluctuations in raw materials, feeding processes, and unforeseen disruptions, along with the inherent spatio-temporal dynamics of sensor data. This article addresses these challenges by proposing a collaborative anomaly monitoring architecture that leverages the cloud, edge devices, and a powerful algorithm: The spatio-temporal attention-based (STA) minimal gated unit (MGU) adversarial autoencoder (AAE). The proposed algorithm, trained in the cloud, analyzes sensor data encompassing information, material, and energy flows within the HPGR. Its core strength lies in capturing the intricate interplay between spatial and temporal data patterns through a novel spatio-temporal attention mechanism. In addition, adversarial training enhances the model's ability to distinguish normal operations from anomalies. Edge devices perform real-time monitoring and transmit preprocessed data to the cloud for STA-MGU-AAE analysis. The extracted features not only enable accurate anomaly detection in process variables, but also facilitate root cause analysis, leading to significant improvements in process stability and reliability. The effectiveness of the proposed architecture is validated through practical beneficiation experiments, demonstrating its potential to revolutionize HPGR anomaly monitoring in production processes.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2024.3514161