An Integrated Distributed Fault Diagnosis Framework for Large-Scale Industrial Processes Based on Spatio-Temporal Causal Analysis

The networked structure of sensors emerges in large-scale industrial processes. Causal graphs can reveal the underlying mechanisms. However, due to the constraints of material and information flows, industrial process data exhibit complex spatio-temporal characteristics. Traditional causal discovery...

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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 21; H. 8; S. 6537 - 6546
Hauptverfasser: Hua, Dongjie, Dong, Jie, Peng, Kaixiang, Simani, Silvio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.08.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Convolutional neural networks
Correlation
Decoding
Distributed fault diagnosis
Fault detection
Fault diagnosis
Feature extraction
Hot strip mills
Information flow
large-scale industrial process
Redundancy
root cause recognition
Spatiotemporal data
spatio–temporal causal graph (STCG)
spatio–temporal characteristics
Strip mills
Time series analysis
Training
Vegetation
ISSN:1551-3203, 1941-0050
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Zusammenfassung:The networked structure of sensors emerges in large-scale industrial processes. Causal graphs can reveal the underlying mechanisms. However, due to the constraints of material and information flows, industrial process data exhibit complex spatio-temporal characteristics. Traditional causal discovery results include redundant information and the spatio-temporal features are not sufficiently mined, affecting the accuracy of fault diagnosis. To address the above problems, an integrated distributed fault diagnosis framework is proposed. First, a new method combining mechanism knowledge and correlation is proposed to construct a spatio-temporal causal graph, which highlight spatio-temporal causal information. Second, an embedded time convolutional network-based autoencoder is designed to extract spatio-temporal features simultaneously. Then, the local-global fault detection scheme is performed. On this basis, a new anomaly status information matrix is designed by decoder and spatial features to achieve root cause recognition. Finally, the effectiveness of the proposed method is validated using actual data from the hot strip mill process, achieving a fault detection accuracy of 98.3<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula>.
Bibliographie:ObjectType-Article-1
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2025.3567381