__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. Preface Acknowledgments Introduction Example-Driven Exploration Problem Formulation Applications of Example-Based Methods Road Map Example-Based Approaches Relational Data Preliminaries Reverse Engineering Queries (REQ) Exact Reverse Engineering Approximate Reverse Engineering Schema Mapping From Schema Mapping to Examples Example-Driven Schema Mapping Data Cleaning Entity Matching Interactive Data Repairing Example-Based Data Exploration Systems Summary Graph Data The Graph Data Model Search by Example Nodes Connectivity and Closeness Clusters and Node Attributes Similar Entity Search in Information Graphs Reverse Engineering Queries on Graphs Learning Path Queries on Graphs Reverse Engineering SPARQL Queries Search by Example Structures Graph Query via Entity-Tuples Queries with Example Subgraphs Summary Textual Data Documents as Examples Learning Relevance from Plain-Text Modeling Networks of Document Semi-Structured Data as Example Relation Extraction Incomplete Web Tables Summary Unifying Example-Based Approaches Data Model Conversion Seeking Relations Implicit Relation Explicit Relation Entity Extraction and Matching Open Research Directions Online Learning Passive Learning First- and Second-Order Learning Regularization MindReader Multi-View Learning Active Learning Multi-Armed Bandits Gaussian Processes Explore-by-Example The Road Ahead Supporting Interactive Explorations Query Processing Automatic Result Analysis Presenting Answers and Exploration Alternatives Results Presentation Generation of Exploration Alternatives New Challenges Explore Heterogeneous Data Personalized Explorations Exploration for Everybody Conclusions Bibliography Authors' Biographies 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. Outlines latent variable based hypothesis testing fault detection techniques to enhance monitoring processes represented by linear or nonlinear input-space models (such as PCA) or input-output models (such as PLS) Explains multiscale latent variable based hypothesis testing fault detection techniques using multiscale representation to help deal with uncertainty in the data and minimize its effect on fault detection Includes interval PCA (IPCA) and interval PLS (IPLS) fault detection methods to enhance the quality of fault detection Provides model-based detection techniques for the improvement of monitoring processes using state estimation-based fault detection approaches Demonstrates the effectiveness of the proposed strategies by conducting simulation and experimental studies on synthetic data
