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.
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 Students 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
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.
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.
