Quality-Relevant Modeling and Monitoring of Industrial Cyber-Physical Systems: The Semi-Supervised Dynamic Latent Variable Models

In modern industrial cyber-physical systems, a mass of process variables has been obtained by the high-sampling online sensors. Meanwhile, the key quality indexes are usually obtained infrequently from the laboratory. Hence, these quality variables are with low sampling rate. To avail of the complet...

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Bibliographic Details
Published in:IEEE transactions on industrial cyber-physical systems 2025, Vol.3, p.39-47
Main Authors: Zhou, Le, Wang, Yaoxin, Wu, Yuanqing, He, Shenghuang, Song, Zhihuan
Format: Article
Language:English
Subjects:
Analytical models
Cyber-physical systems
fault detection
Indexes
Kalman filters
Noise
Numerical models
Parameter estimation
Probabilistic logic
Process monitoring
semi-supervised model
Time series analysis
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Summary:In modern industrial cyber-physical systems, a mass of process variables has been obtained by the high-sampling online sensors. Meanwhile, the key quality indexes are usually obtained infrequently from the laboratory. Hence, these quality variables are with low sampling rate. To avail of the complete process and quality variables with various sampling rates in the dynamic processes, a set of semi-supervised dynamic latent variable models are proposed for dynamic modeling and quality-relevant monitoring. The proposed models have built a unified structure to consider both the auto-correlations and cross-correlations between the process and quality variables with unbalanced sampling sizes. Hence, the feature extraction of the time series data is dynamically adjusted under the guidance of the quality variables. Then, the quality-relevant monitoring schemes are proposed, which is validated by a numerical case and an actual wastewater treatment process.
ISSN:2832-7004
2832-7004
DOI:10.1109/TICPS.2024.3501275