Data-Driven and Model-Based Methods for Fault Detection and Diagnosis covers techniques that improve the quality of fault detection and enhance monitoring through chemical and environmental processes. The book provides both the theoretical framework and technical solutions. It starts with a review of relevant literature, proceeds with a detailed description of developed methodologies, and then discusses the results of developed methodologies, and ends with major conclusions reached from the analysis of simulation and experimental studies. The book is an indispensable resource for researchers in academia and industry and practitioners working in chemical and environmental engineering to do their work safely.
1. Chapter 1: Introduction - p. 1 2. Chapter 2: PCA and PLS-based generalized likelihood ratio for fault detection - p. 11 3. Chapter 3: Kernel PCA- and Kernel PLS-based generalized likelihood ratio tests for fault detection - p. 49 4. Chapter 4: Linear and nonlinear multiscale latent variable-based generalized likelihood ratio for fault detection - p. 79 5. Chapter 5: Linear and nonlinear interval latent variable approaches for fault detection - p. 135 6. Chapter 6: Model-based approaches for fault detection - p. 221 7. Chapter 7: Conclusions and perspectives - p. 259 8. Appendix - p. 279 9. Index - p. 289
Data-Driven and Model-Based Methods for Fault Detection and Diagnosis covers techniques that improve the quality of fault detection and enhance monitoring through chemical and environmental processes. The book provides both the theoretical framework and technical solutions. It starts with a review of relevant literature, proceeds with a detailed description of developed methodologies, and then discusses the results of developed methodologies, and ends with major conclusions reached from the analysis of simulation and experimental studies. The book is an indispensable resource for researchers in academia and industry and practitioners working in chemical and environmental engineering to do their work safely.
1. Chapter 1: Introduction - p. 1 2. Chapter 2: PCA and PLS-based generalized likelihood ratio for fault detection - p. 11 3. Chapter 3: Kernel PCA- and Kernel PLS-based generalized likelihood ratio tests for fault detection - p. 49 4. Chapter 4: Linear and nonlinear multiscale latent variable-based generalized likelihood ratio for fault detection - p. 79 5. Chapter 5: Linear and nonlinear interval latent variable approaches for fault detection - p. 135 6. Chapter 6: Model-based approaches for fault detection - p. 221 7. Chapter 7: Conclusions and perspectives - p. 259 8. Appendix - p. 279 9. Index - p. 289
