Spatio-Temporal Learning and Monitoring for Complex Dynamic Processes with Irregular Data
- 1st Edition - July 25, 2025
- Latest edition
- Authors: Chunhui Zhao, Wanke Yu
- Language: English
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
Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes introduces learning, modeling, and monitoring methods for highly complex dynamic processes with irregular data. Two classes of robust modeling methods are highlighted, including low-rank characteristic of matrices and heavy-tailed characteristic of distributions. In this class, the missing data, ambient noise, and outlier problems are solved using low-rank matrix complement for monitoring model development. Secondly, the Laplace distribution is explored, which is adopted to measure the process uncertainty to develop robust monitoring models.
The book not only discusses the complex models but also their real-world applications in industry.
The 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
Practitioner engaged in complex industrial process monitoring
1. Background
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
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
- Latest edition
- Published: July 25, 2025
- Language: English
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.
Affiliations and expertise
The State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, ChinaWY
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.
Affiliations and expertise
The State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, ChinaRead Spatio-Temporal Learning and Monitoring for Complex Dynamic Processes with Irregular Data on ScienceDirect