Data Driven and Model Based Methods for Fault Detection and Diagnosis

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

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  • Author : Majdi Mansouri
  • Publisher : Elsevier
  • Pages : 322 pages
  • ISBN : 0128191651
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKData Driven and Model Based Methods for Fault Detection and Diagnosis

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

Data-Driven and Model-Based Methods for Fault Detection and Diagnosis
  • Author : Majdi Mansouri,Mohamed-Faouzi Harkat,Hazem Nounou,Mohamed N. Nounou
  • Publisher : Elsevier
  • Release : 05 February 2020
GET THIS BOOKData-Driven and Model-Based Methods for Fault Detection and Diagnosis

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

Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems

Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems
  • Author : Steven X. Ding
  • Publisher : Springer Science & Business Media
  • Release : 12 April 2014
GET THIS BOOKData-driven Design of Fault Diagnosis and Fault-tolerant Control Systems

Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the

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

Data-Driven and Model-Based Methods for Fault Detection and Diagnosis
  • Author : Majdi Mansouri,Mohamed-Faouzi Harkat,Hazem N Nounou,Mohamed N Nounou
  • Publisher : Elsevier
  • Release : 28 February 2020
GET THIS BOOKData-Driven and Model-Based Methods for Fault Detection and Diagnosis

The main objective of Data-Driven and Model-Based Methods for Fault Detection and Diagnosis is to develop techniques that improve the quality of fault detection and then utilize these developed techniques to enhance monitoring various chemical and environmental processes. The book provides both the theoretical framework and technical solutions. It starts with reviewing relevant literature, proceeds with a detailed description of developed methodologies, followed by a discussion of the results of developed methodologies, and ends with major conclusions reached from the

Diagnosis and Fault-tolerant Control 1

Diagnosis and Fault-tolerant Control 1
  • Author : Vicenc Puig,Silvio Simani
  • Publisher : John Wiley & Sons
  • Release : 01 December 2021
GET THIS BOOKDiagnosis and Fault-tolerant Control 1

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 safety to ensure efficient operations. Moreover, the need for a high degree of tolerance with respect to possible faults represents a further key point, primarily for complex systems, as modeling and control are inherently challenging, and maintenance is both

Data-Driven Fault Detection for Industrial Processes

Data-Driven Fault Detection for Industrial Processes
  • Author : Zhiwen Chen
  • Publisher : Springer
  • Release : 02 January 2017
GET THIS BOOKData-Driven Fault Detection for Industrial Processes

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 been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA)

Model-Based Fault Diagnosis Techniques

Model-Based Fault Diagnosis Techniques
  • Author : Steven X. Ding
  • Publisher : Springer Science & Business Media
  • Release : 20 December 2012
GET THIS BOOKModel-Based Fault Diagnosis Techniques

Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools. This second edition of Model-Based Fault Diagnosis Techniques contains: • new material on fault isolation and identification and alarm

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

Data-Driven Fault Detection and Reasoning for Industrial Monitoring
  • Author : Jing Wang,Jinglin Zhou,Xiaolu Chen
  • Publisher : Springer
  • Release : 04 January 2022
GET THIS BOOKData-Driven Fault Detection and Reasoning for Industrial Monitoring

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this

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

Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes
  • Author : Evan L. Russell,Leo H. Chiang,Richard D. Braatz
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKData-driven Methods for Fault Detection and Diagnosis in Chemical Processes

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 from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process-monitoring techniques presented include: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis. The text

Advanced methods for fault diagnosis and fault-tolerant control

Advanced methods for fault diagnosis and fault-tolerant control
  • Author : Steven X. Ding
  • Publisher : Springer
  • Release : 24 November 2020
GET THIS BOOKAdvanced methods for fault diagnosis and fault-tolerant control

The major objective of this book is to introduce advanced design and (online) optimization methods for fault diagnosis and fault-tolerant control from different aspects. Under the aspect of system types, fault diagnosis and fault-tolerant issues are dealt with for linear time-invariant and time-varying systems as well as for nonlinear and distributed (including networked) systems. From the methodological point of view, both model-based and data-driven schemes are investigated.To allow for a self-contained study and enable an easy implementation in real

Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control

Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control
  • Author : Ch. Venkateswarlu,Rama Rao Karri
  • Publisher : Elsevier
  • Release : 31 January 2022
GET THIS BOOKOptimal State Estimation for Process Monitoring, Fault Diagnosis and Control

Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control presents various mechanistic model based state estimators and data-driven model based state estimators with a special emphasis on their development and applications to process monitoring, fault diagnosis and control. The design and analysis of different state estimators are highlighted with a number of applications and case studies concerning to various real chemical and biochemical processes. The book starts with the introduction of basic concepts, extending to classical methods and successively

Advanced methods for fault diagnosis and fault-tolerant control

Advanced methods for fault diagnosis and fault-tolerant control
  • Author : Steven X. Ding
  • Publisher : Springer Nature
  • Release : 24 November 2020
GET THIS BOOKAdvanced methods for fault diagnosis and fault-tolerant control

The major objective of this book is to introduce advanced design and (online) optimization methods for fault diagnosis and fault-tolerant control from different aspects. Under the aspect of system types, fault diagnosis and fault-tolerant issues are dealt with for linear time-invariant and time-varying systems as well as for nonlinear and distributed (including networked) systems. From the methodological point of view, both model-based and data-driven schemes are investigated.To allow for a self-contained study and enable an easy implementation in real

Fault Detection and Diagnosis in Industrial Systems

Fault Detection and Diagnosis in Industrial Systems
  • Author : L.H. Chiang,E.L. Russell,R.D. Braatz
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKFault Detection and Diagnosis in Industrial Systems

Early and accurate fault detection and diagnosis for modern chemical plants can minimize downtime, increase the safety of plant operations, and reduce manufacturing costs. This book presents the theoretical background and practical techniques for data-driven process monitoring. It demonstrates the application of all the data-driven process monitoring techniques to the Tennessee Eastman plant simulator, and looks at the strengths and weaknesses of each approach in detail. A plant simulator and problems allow readers to apply process monitoring techniques.

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery
  • Author : Yaguo Lei
  • Publisher : Butterworth-Heinemann
  • Release : 02 November 2016
GET THIS BOOKIntelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery provides a comprehensive introduction of intelligent fault diagnosis and RUL prediction based on the current achievements of the author's research group. The main contents include multi-domain signal processing and feature extraction, intelligent diagnosis models, clustering algorithms, hybrid intelligent diagnosis strategies, and RUL prediction approaches, etc. This book presents fundamental theories and advanced methods of identifying the occurrence, locations, and degrees of faults, and also includes information on how to

Data-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains

Data-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains
  • Author : Hongtian Chen,Bin Jiang,Ningyun Lu,Wen Chen
  • Publisher : Springer Nature
  • Release : 25 April 2020
GET THIS BOOKData-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains

This book addresses the needs of researchers and practitioners in the field of high-speed trains, especially those whose work involves safety and reliability issues in traction systems. It will appeal to researchers and graduate students at institutions of higher learning, research labs, and in the industrial R&D sector, catering to a readership from a broad range of disciplines including intelligent transportation, electrical engineering, mechanical engineering, chemical engineering, the biological sciences and engineering, economics, ecology, and the mathematical sciences.