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

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

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

  • Publisher : Elsevier
  • Release : 2020-02-05
  • Pages : 322
  • ISBN : 0128191651
  • Language : En, Es, Fr & De
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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

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

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

by Steven X. Ding

  • Publisher : Springer Science & Business Media
  • Release : 2014-04-12
  • Pages : 300
  • ISBN : 1447164105
  • Language : En, Es, Fr & De
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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 rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and demonstrated on industrial case systems. Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems will be of interest to process and control engineers, engineering students and researchers with a control engineering background.

Advanced methods for fault diagnosis and fault-tolerant control

Advanced methods for fault diagnosis and fault-tolerant control
A Book

by Steven X. Ding

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 3662620049
  • Language : En, Es, Fr & De
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Fault Detection and Diagnosis in Industrial Systems

Fault Detection and Diagnosis in Industrial Systems
A Book

by L.H. Chiang,E.L. Russell,R.D. Braatz

  • Publisher : Springer Science & Business Media
  • Release : 2012-12-06
  • Pages : 279
  • ISBN : 1447103475
  • Language : En, Es, Fr & De
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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.

Data-Driven Design of Fault Diagnosis Systems

Data-Driven Design of Fault Diagnosis Systems
Nonlinear Multimode Processes

by Adel Haghani Abandan Sari

  • Publisher : Springer Science & Business
  • Release : 2014-04-22
  • Pages : 136
  • ISBN : 3658058072
  • Language : En, Es, Fr & De
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In many industrial applications early detection and diagnosis of abnormal behavior of the plant is of great importance. During the last decades, the complexity of process plants has been drastically increased, which imposes great challenges in development of model-based monitoring approaches and it sometimes becomes unrealistic for modern large-scale processes. The main objective of Adel Haghani Abandan Sari is to study efficient fault diagnosis techniques for complex industrial systems using process historical data and considering the nonlinear behavior of the process. To this end, different methods are presented to solve the fault diagnosis problem based on the overall behavior of the process and its dynamics. Moreover, a novel technique is proposed for fault isolation and determination of the root-cause of the faults in the system, based on the fault impacts on the process measurements.

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

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

by Evan L. Russell,Leo H. Chiang,Richard D. Braatz

  • Publisher : Springer
  • Release : 2000-02-25
  • Pages : 192
  • ISBN : 1852332581
  • Language : En, Es, Fr & De
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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 demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process-monitoring techniques to a nontrivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques. The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application.

Data-Driven Fault Detection for Industrial Processes

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

by Zhiwen Chen

  • Publisher : Springer
  • Release : 2017-01-02
  • Pages : 112
  • ISBN : 3658167564
  • Language : En, Es, Fr & De
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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) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed.

Fault Diagnosis of Hybrid Dynamic and Complex Systems

Fault Diagnosis of Hybrid Dynamic and Complex Systems
A Book

by Moamar Sayed-Mouchaweh

  • Publisher : Springer
  • Release : 2018-03-27
  • Pages : 286
  • ISBN : 3319740148
  • Language : En, Es, Fr & De
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Online fault diagnosis is crucial to ensure safe operation of complex dynamic systems in spite of faults affecting the system behaviors. Consequences of the occurrence of faults can be severe and result in human casualties, environmentally harmful emissions, high repair costs, and economical losses caused by unexpected stops in production lines. The majority of real systems are hybrid dynamic systems (HDS). In HDS, the dynamical behaviors evolve continuously with time according to the discrete mode (configuration) in which the system is. Consequently, fault diagnosis approaches must take into account both discrete and continuous dynamics as well as the interactions between them in order to perform correct fault diagnosis. This book presents recent and advanced approaches and techniques that address the complex problem of fault diagnosis of hybrid dynamic and complex systems using different model-based and data-driven approaches in different application domains (inductor motors, chemical process formed by tanks, reactors and valves, ignition engine, sewer networks, mobile robots, planetary rover prototype etc.). These approaches cover the different aspects of performing single/multiple online/offline parametric/discrete abrupt/tear and wear fault diagnosis in incremental/non-incremental manner, using different modeling tools (hybrid automata, hybrid Petri nets, hybrid bond graphs, extended Kalman filter etc.) for different classes of hybrid dynamic and complex systems.

Fault-Diagnosis Applications

Fault-Diagnosis Applications
Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems

by Rolf Isermann

  • Publisher : Springer Science & Business Media
  • Release : 2011-04-06
  • Pages : 354
  • ISBN : 9783642127670
  • Language : En, Es, Fr & De
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Supervision, condition-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes and vehicles in order to improve reliability, availability, maintenance and lifetime. For safety-related processes fault-tolerant systems with redundancy are required in order to reach comprehensive system integrity. This book is a sequel of the book “Fault-Diagnosis Systems” published in 2006, where the basic methods were described. After a short introduction into fault-detection and fault-diagnosis methods the book shows how these methods can be applied for a selection of 20 real technical components and processes as examples, such as: Electrical drives (DC, AC) Electrical actuators Fluidic actuators (hydraulic, pneumatic) Centrifugal and reciprocating pumps Pipelines (leak detection) Industrial robots Machine tools (main and feed drive, drilling, milling, grinding) Heat exchangers Also realized fault-tolerant systems for electrical drives, actuators and sensors are presented. The book describes why and how the various signal-model-based and process-model-based methods were applied and which experimental results could be achieved. In several cases a combination of different methods was most successful. The book is dedicated to graduate students of electrical, mechanical, chemical engineering and computer science and for engineers.

Data Driven Self-improving Fault Detection and Diagnosis Methodologies in Complex Manufacturing Process

Data Driven Self-improving Fault Detection and Diagnosis Methodologies in Complex Manufacturing Process
A Book

by Nong Jin

  • Publisher : Unknown Publisher
  • Release : 2006
  • Pages : 167
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Machine Learning and Optimization Algorithms for Fault Diagnosis and Prognosis in Automotive Systems

Machine Learning and Optimization Algorithms for Fault Diagnosis and Prognosis in Automotive Systems
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2015
  • Pages : 354
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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On-line Fault Detection and Supervision in the Chemical Process Industries, 2001

On-line Fault Detection and Supervision in the Chemical Process Industries, 2001
(CHEMFAS-4) : a Proceedings Volume from the 4th IFAC Workshop, Jejudo Island, Korea, 7-8 June 2001

by G. Stephanopoulos,José Alberto Romagnoli,En Sup Yoon

  • Publisher : Elsevier Science Limited
  • Release : 2001
  • Pages : 376
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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This proceedings contains papers from the IFAC Symposium on On-line Fault Detection and Supervision in the Chemical Process Industries (CHEMFAS-4), held in Jejudo Island, Korea, 7-8 June 2001. The proceedings includes theoretical contributions, as well as a wide range of industrial applications in process fault diagnosis, monitoring, and advanced supervision. The papers are organized around the following themes: fault detection and diagnosis, statistical and trend analysis, methodologies, sensor location and data reconciliation and applications. The driving forces for on-line fault detection and improved supervision of process operation include human safety, environmental safeguards, and equipment protection, as well as economic considerations such as the improvement of product quality, increased production, and so on. These diverse incentives, together with the development and evaluation of novel methodologies for on-line process supervision and management, form the focus of the symposium and of the papers in this Proceedings. Altogether over 60 papers are presented, covering strategies including model-based and data-driven approaches, as well as knowledge-based systems, statistical techniques, and AI-based pattern recognition techniques. All the work presented is at the cutting edge of research in this dynamic field.

Signature-driven Fault Management Methodologies for Complex Engineering Systems

Signature-driven Fault Management Methodologies for Complex Engineering Systems
A Book

by Zhiguo Li

  • Publisher : Unknown Publisher
  • Release : 2007
  • Pages : 197
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Nonlinear Model-based Process Control

Nonlinear Model-based Process Control
Applications in Petroleum Refining

by Rashid M. Ansari,Moses O. Tade

  • Publisher : Springer
  • Release : 2000-04-12
  • Pages : 232
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The last decade has seen considerable interest in reviving the fortunes of non linear control. In contrast to the approaches of the 60S, 70S and 80S a very pragmatic agenda for non-linear control is being pursued using the model-based predictive control paradigm. This text by R. Ansari and M. Tade gives an excellent synthesis of this new direction. Two strengths emphasized by the text are: (i) four applications found in refinery processes are used to give the text a firm practical continuity; (ii) a non-linear model-based control architecture is used to give the method a coherent theoretical framework.

A Model-based Fault Detection and Diagnosis Methodology for HVAC Subsystems

A Model-based Fault Detection and Diagnosis Methodology for HVAC Subsystems
A Book

by Ian Blair Dwight McIntosh

  • Publisher : Unknown Publisher
  • Release : 2000
  • Pages : 230
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Principles of Data Mining and Knowledge Discovery

Principles of Data Mining and Knowledge Discovery
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1997
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Data-driven Whole Building Fault Detection and Diagnosis

Data-driven Whole Building Fault Detection and Diagnosis
A Book

by Yimin Chen

  • Publisher : Unknown Publisher
  • Release : 2019
  • Pages : 500
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Residential and commercial buildings are responsible for more than 40% of the primary energy consumption in the United States. Energy wastes are estimated to reach 15% to 30% of total energy consumption due to malfunctioning sensors, components, and control systems, as well as degrading components in Heating, Ventilation, Air-conditioning (HVAC) systems and lighting systems in commercial buildings in the U.S. Studies have demonstrated that a large energy saving can be achieved by automated fault detection and diagnosis (AFDD) followed by corrections. Field studies have shown that, AFDD tools can help to reach energy savings by 5-30% from different systems such as HVAC systems, lighting systems, and refrigeration systems. At the same time, the deployment of AFDD tools can also significantly improve indoor air quality, reduce peak demand, and lower pollution. In buildings, many components or equipment are closely coupled in a HVAC system. Because of the coupling, a fault happening in one component might propagate and affect other components or subsystems. In this study, a whole building fault (WBF) is defined as a fault that occurs in one component or equipment but causes fault impacts (abnormalities) on other components and subsystems, or causes significant impacts on energy consumption and/or indoor air quality. Over the past decades, extensive research have been conducted on the development of component AFDD methods and tools. However, whole building AFDD methods, which can detect and diagnose a WBF, have not been well studied. Existing component level AFDD solutions often fail to detect a WBF or generate a high false alarm rate. Isolating a WBF is also very challenging by using component level AFDD solutions. Even with the extensive research, cost-effectiveness and scalability of existing AFDD methods are still not satisfactory. Therefore, the focus of this research is to develop cost-effective and scalable solutions for WBF AFDD. This research attempts to integrate data-driven methods with expert knowledge/rules to overcome the above-mentioned challenges. A suite of WBF AFDD methods have hence been developed, which include: 1) a weather and schedule based pattern matching method and feature based Principal Component Analysis (WPM-FPCA) method for whole building fault detection. The developed WPM-FPCA method successfully overcome the challenges such as the generation of accurate and dynamic baseline and data dimensionality reduction. And 2) a data-driven and expert knowledge/rule based method using both Bayesian Network (BN) and WPM for WBF diagnosis. The developed WPM-BN method includes a two-layer BN structure model and BN parameter model which are either learned from baseline data or developed from expert knowledge. Various WBFs have been artificially implemented in a real demo building. Building operation data which include baseline data, data that contain naturally-occurred faults and artificially implemented faults are collected and analyzed. Using the collected real building data, the developed methods are evaluated. The evaluation demonstrates the efficacy of the developed methods to detect and diagnose a WBF, as well as their implementation cost-effectiveness.

Journal of Chemical Engineering of Japan

Journal of Chemical Engineering of Japan
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2008
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Includes abstracts of Kagaku kōgaku, v. 31-

Technometrics

Technometrics
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2002
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Intelligent Systems and Automation

Intelligent Systems and Automation
2nd Mediterranean Conference on Intelligent Systems and Automation (CISA '09)

by Lotfi Beji,Samir Otmane,Azgal Abichou

  • Publisher : Amer Inst of Physics
  • Release : 2009-03-09
  • Pages : 393
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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The aim of CISA’09 is to present the latest research and application results emerging from new research and technological developments of complex systems and intelligent machines acting on known or unknown, virtual or real, environments in an autonomous way or in cooperation with humans. This field requires skills in automation and control, perception of the environment, human-computer interfaces, mechanics and design, simulation, etc. It also aims at encouraging scientific cooperation between North and South and promoting scientific exchanges through a durable event.