
Spatio-Temporal Learning and Monitoring for Complex Dynamic Processes with Irregular Data
- 1st Edition - July 1, 2025
- Imprint: Academic Press
- Authors: Chunhui Zhao, Wanke Yu
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 6 7 5 - 1
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 6 7 6 - 8
Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes introduces learning, modeling, and monitoring methods for highly complex dynamic processes with irregu… Read more

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Request a sales quoteThe book not only discusses the complex models but also their real-world applications in industry.
- Shows how to analyze, in great detail, the industrial operational status through spatio-temporal representation learning
- Covers how to establish robust monitoring models for industrial processes with irregular data
- Indicates how to adaptively update models in order to reduce frequent false alarms for dynamic processes
- Explains how to take the temporal correlation into consideration to develop an adaptive monitoring model for satisfying the dynamic behaviours of industrial processes
2. Low-rank characteristic and temporal correlation analytics for incipient industrial fault detection with missing data
3. A robust dissimilarity distribution analytics with Laplace distribution for incipient fault detection
4. Variational Bayesian Student’s t-mixture model with closed-form missing value imputation for robust process monitoring of low-quality data
5. Stationary subspace analysis based hierarchical model for batch process monitoring
6. Recursive cointegration analytics for adaptive monitoring of nonstationary industrial processes with both static and dynamic variations
7. Incremental variational Bayesian Gaussian mixture model with decremental optimization for distribution accommodation and fine-scale adaptive process monitoring
8. MoniNet with concurrent analytics of temporal and spatial information for fault detection in industrial processes
9. Meticulous process monitoring with multiscale convolutional feature extraction
10. Summary and prospect
- Edition: 1
- Published: July 1, 2025
- Imprint: Academic Press
- No. of pages: 300
- Language: English
- Paperback ISBN: 9780443336751
- eBook ISBN: 9780443336768
CZ
Chunhui Zhao
Chunhui Zhao is a Qiushi distinguished professor at Zhejiang University in China, and an expert in intelligent industrial monitoring with 20 years of experience in this field. She has authored or co-authored more than 400 papers in peer-reviewed international journals and conferences. Her research interests include statistical machine learning and data mining for industrial applications.
WY
Wanke Yu
Wanke Yu is a research fellow at the School of Electrical & Electronic Engineering, Nanyang Technological University in Singapore. Wanke Yu received his Ph.D. degree in automatic control from Zhejiang University, Hangzhou, China, in 2020. His research interests include probabilistic graphic model, deep neural network, and nonconvex optimization, and their applications to process control.