Data-Driven and Model-Based Methods for Fault Detection and Diagnosis

✍ Scribed by Majdi Mansouri, Mohamed-Faouzi Harkat, Hazem N. Nounou, Mohamed N. Nounou

Publisher: Elsevier
Year: 2020
Tongue: English
Leaves: 166
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

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

📜 SIMILAR VOLUMES

Data-Driven and Model-Based Methods for

📁 Data-Driven and Model-Based Methods for Fault Detection and Diagnosis

✍ Majdi Mansouri, Mohamed-Faouzi Harkat, Hazem N. Nounou, Mohamed N. Nounou 📂 Library 📅 2020 🏛 Elsevier 🌐 English

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

Data-driven Methods for Fault Detection

📁 Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes

✍ Evan L. Russell PhD, Leo H. Chiang MS, Richard D. Braatz PhD (auth.) 📂 Library 📅 2000 🏛 Springer-Verlag London 🌐 English

Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely f

Diagnosis and Fault-tolerant Control, Vo

📁 Diagnosis and Fault-tolerant Control, Volume 1: Data-driven and Model-based Fault Diagnosis Techniques

✍ Vicenç Puig, Silvio Simani 📂 Library 📅 2022 🏛 Wiley-ISTE 🌐 English

This book presents recent advances in fault diagnosis strategies for complex dynamic systems. Its impetus derives from the need for an overview of the challenges of the fault diagnosis technique, especially for those demanding systems that require reliability, availability, maintainability and safet

Data-Driven Fault Detection for Industri

📁 Data-Driven Fault Detection for Industrial Processes: Canonical Correlation Analysis and Projection Based Methods

✍ Zhiwen Chen (auth.) 📂 Library 📅 2017 🏛 Springer Vieweg 🌐 English

Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has b

Recent Developments in Model-Based and D

📁 Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis

✍ Didier Theilliol; Józef Korbicz; Janusz Kacprzyk 📂 Library 📅 2023 🏛 Springer Nature 🌐 English

The book consists of recent works on several axes either with a more theoretical nature or with a focus on applications, which will span a variety of up-to-date topics in the field of systems and control. The main market area of the contributions include: Advanced fault-tolerant control, control rec

Recent Developments in Model-Based and D

📁 Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis

✍ Didier Theilliol, Józef Korbicz, Janusz Kacprzyk 📂 Library 📅 2023 🏛 Springer 🌐 English

The book consists of recent works on several axes either with a more theoretical nature or with a focus on applications, which will span a variety of up-to-date topics in the field of systems and control.The main market area of the contributions include:<sp