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.

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