Spatio-Temporal Learning and Monitoring for Complex Dynamic Processes with Irregular Data

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.

• 1. Background2. Low-rank characteristic and temporal correlation analytics for incipient industrial fault detection with missing data3. A robust dissimilarity distribution analytics with Laplace distribution for incipient fault detection4. Variational Bayesian Student’s t-mixture model with closed-form missing value imputation for robust process monitoring of low-quality data5. Stationary subspace analysis based hierarchical model for batch process monitoring6. Recursive cointegration analytics for adaptive monitoring of nonstationary industrial processes with both static and dynamic variations7. Incremental variational Bayesian Gaussian mixture model with decremental optimization for distribution accommodation and fine-scale adaptive process monitoring8. MoniNet with concurrent analytics of temporal and spatial information for fault detection in industrial processes9. Meticulous process monitoring with multiscale convolutional feature extraction10. Summary and prospect