Chapter 1 Introduction -- Chapter 2 An Overview of Conventional MSPC Methods -- Chapter 3 System-wide Process Monitoring and Fault Diagnosis -- Chapter 4 Quality-Related Time-Varying Process Monitoring -- Chapter 5 Quality-Related Dynamic Process Monitoring: Part I -- Chapter 6 Quality-Related Dynamic Process Monitoring: Part II -- Chapter 7 Quality-Related Complex Nonlinear Process Monitoring -- Chapter 8 Quality-Related Fault Subspace Extraction for Fault Diagnosis -- Chapter 9 Non-Gaussian Process Monitoring and Fault Diagnosis -- Chapter 10 Hybrid Gaussian/Non-Gaussian Quality-Related Nonlinear Process Monitoring -- Chapter 11 Conclusions and Future Work.

This book reports recent developments of the multivariate statistical process control (MSPC) methods for industrial process monitoring and fault diagnosis. Specifically, this book gives an overview of recently developed methods in different aspects, namely system-wide process monitoring, quality-related time-varying process monitoring, quality-related dynamic process monitoring, quality-related complex nonlinear process monitoring, and quality-related fault subspace extraction for fault diagnosis, non-Gaussian process monitoring and fault diagnosis, etc. In order to help readers understand and master the new methods, before presenting each new method in each chapter, a specialized section is provided to review the closely related several basis models. Throughout the book, detailed steps of new methods are provided, and all new algorithms or methods proposed by us are tested and verified by numerical simulations or Tennessee Eastman benchmark chemical process. Readers find illustrative demonstration examples on a range of industrial processes to study and the present deficiency and recent developments of the MSPC methods for industrial processes monitoring and fault diagnosis, by learning from the authors’ latest achievements or new methods around the practical industrial needs. This book is assimilated by advanced undergraduates and graduate students, as well as industrial and process engineering researchers and practitioners.