Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes

Process monitoring is used to analyse the operational status of the process system and provide an early warning in order to prevent industrial accidents. It is therefore an effective means to facilitate efficient, safe and optimal operation of industrial processes. Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes introduces learning, modelling and monitoring methods for highly complex dynamic processes that have irregular data. Two classes of robust modelling methods are included in the book: firstly, 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, 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