Data Fusion for Enhanced Aircraft Engine Prognostics and Health Management

Data Fusion for Enhanced Aircraft Engine Prognostics and Health Management

Author: National Aeronautics and Space Administration (NASA)

Publisher: Createspace Independent Publishing Platform

Published: 2018-05-29

Total Pages: 36

ISBN-13: 9781720455578

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Aircraft gas-turbine engine data is available from a variety of sources, including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data fusion is the integration of data or information from multiple sources for the achievement of improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/ information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This report describes a basic PHM data fusion architecture being developed in alignment with the NASA C-17 PHM Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center, NASA Dryden Flight Research Center, and Pratt & Whitney have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion, as it applies to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This report will provide a chronology and summary of the work accomplished under this research contract.Volponi, AlGlenn Research CenterARTIFICIAL INTELLIGENCE; INFORMATION SYSTEMS; PROPULSION; RELIABILITY; STATISTICAL ANALYSIS; CHRONOLOGY; FLIGHT TESTS; FUZZY SYSTEMS; GAS TURBINE ENGINES


Book Synopsis Data Fusion for Enhanced Aircraft Engine Prognostics and Health Management by : National Aeronautics and Space Administration (NASA)

Download or read book Data Fusion for Enhanced Aircraft Engine Prognostics and Health Management written by National Aeronautics and Space Administration (NASA) and published by Createspace Independent Publishing Platform. This book was released on 2018-05-29 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aircraft gas-turbine engine data is available from a variety of sources, including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data fusion is the integration of data or information from multiple sources for the achievement of improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/ information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This report describes a basic PHM data fusion architecture being developed in alignment with the NASA C-17 PHM Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center, NASA Dryden Flight Research Center, and Pratt & Whitney have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion, as it applies to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This report will provide a chronology and summary of the work accomplished under this research contract.Volponi, AlGlenn Research CenterARTIFICIAL INTELLIGENCE; INFORMATION SYSTEMS; PROPULSION; RELIABILITY; STATISTICAL ANALYSIS; CHRONOLOGY; FLIGHT TESTS; FUZZY SYSTEMS; GAS TURBINE ENGINES

Data Fusion for Enhanced Aircraft Engine Prognostics and Health Management

Data Fusion for Enhanced Aircraft Engine Prognostics and Health Management

Author: Al Volponi

Publisher: BiblioGov

Published: 2013-06

Total Pages: 40

ISBN-13: 9781289148034

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Aircraft gas-turbine engine data is available from a variety of sources, including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data fusion is the integration of data or information from multiple sources for the achievement of improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/ information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This report describes a basic PHM data fusion architecture being developed in alignment with the NASA C-17 PHM Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center, NASA Dryden Flight Research Center, and Pratt & Whitney have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion, as it applies to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This report will provide a chronology and summary of the work accomplished under this research contract.


Book Synopsis Data Fusion for Enhanced Aircraft Engine Prognostics and Health Management by : Al Volponi

Download or read book Data Fusion for Enhanced Aircraft Engine Prognostics and Health Management written by Al Volponi and published by BiblioGov. This book was released on 2013-06 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aircraft gas-turbine engine data is available from a variety of sources, including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data fusion is the integration of data or information from multiple sources for the achievement of improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/ information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This report describes a basic PHM data fusion architecture being developed in alignment with the NASA C-17 PHM Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center, NASA Dryden Flight Research Center, and Pratt & Whitney have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion, as it applies to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This report will provide a chronology and summary of the work accomplished under this research contract.

Development of an Information Fusion System for Engine Diagnostics and Health Management

Development of an Information Fusion System for Engine Diagnostics and Health Management

Author: National Aeronautics and Space Administration (NASA)

Publisher: Createspace Independent Publishing Platform

Published: 2018-06-21

Total Pages: 36

ISBN-13: 9781721637003

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Aircraft gas-turbine engine data are available from a variety of sources including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data Fusion is the integration of data or information from multiple sources, to achieve improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This paper describes a basic PHM Data Fusion architecture being developed in alignment with the NASA C17 Propulsion Health Management (PHM) Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center (GRC), NASA Dryden Flight Research Center (DFRC) and Pratt & Whitney (P&W) have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion as applied to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This paper will provide a broad overview of this work, discuss some of the methodologies employed and give some illustrative examples. Volponi, Allan J. and Brotherton, Tom and Luppold, Robert and Simon, Donald L. Glenn Research Center NASA/TM-2004-212924, ARL-TR-3127, E-14364


Book Synopsis Development of an Information Fusion System for Engine Diagnostics and Health Management by : National Aeronautics and Space Administration (NASA)

Download or read book Development of an Information Fusion System for Engine Diagnostics and Health Management written by National Aeronautics and Space Administration (NASA) and published by Createspace Independent Publishing Platform. This book was released on 2018-06-21 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aircraft gas-turbine engine data are available from a variety of sources including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data Fusion is the integration of data or information from multiple sources, to achieve improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This paper describes a basic PHM Data Fusion architecture being developed in alignment with the NASA C17 Propulsion Health Management (PHM) Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center (GRC), NASA Dryden Flight Research Center (DFRC) and Pratt & Whitney (P&W) have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion as applied to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This paper will provide a broad overview of this work, discuss some of the methodologies employed and give some illustrative examples. Volponi, Allan J. and Brotherton, Tom and Luppold, Robert and Simon, Donald L. Glenn Research Center NASA/TM-2004-212924, ARL-TR-3127, E-14364

Development of an Information Fusion System for Engine Diagnostics and Health Management

Development of an Information Fusion System for Engine Diagnostics and Health Management

Author:

Publisher:

Published: 2004

Total Pages: 0

ISBN-13:

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Aircraft gas-turbine engine data are available from a variety of sources including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data Fusion is the integration of data or information from multiple sources, to achieve improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This paper describes a basic PHM Data Fusion architecture being developed in alignment with the NASA C17 Propulsion Health Management (PHM) Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center (GRC), NASA Dryden Flight Research Center (DFRC) and Pratt & Whitney (P & W) have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion as applied to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This paper will provide a broad overview of this work, discuss some of the methodologies employed and give some illustrative examples.


Book Synopsis Development of an Information Fusion System for Engine Diagnostics and Health Management by :

Download or read book Development of an Information Fusion System for Engine Diagnostics and Health Management written by and published by . This book was released on 2004 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aircraft gas-turbine engine data are available from a variety of sources including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data Fusion is the integration of data or information from multiple sources, to achieve improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This paper describes a basic PHM Data Fusion architecture being developed in alignment with the NASA C17 Propulsion Health Management (PHM) Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center (GRC), NASA Dryden Flight Research Center (DFRC) and Pratt & Whitney (P & W) have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion as applied to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This paper will provide a broad overview of this work, discuss some of the methodologies employed and give some illustrative examples.

Integrated System Health Management

Integrated System Health Management

Author: Jiuping Xu

Publisher: Academic Press

Published: 2017-05-18

Total Pages: 473

ISBN-13: 012813268X

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ISHM is an innovative combination of technologies and methods that offers solutions to the reliability problems caused by increased complexities in design, manufacture, use conditions, and maintenance. Its key strength is in the successful integration of reliability (quantitative estimation of successful operation or failure), "diagnosibility" (ability to determine the fault source), and maintainability (how to maintain the performance of a system in operation). It draws on engineering issues such as advanced sensor monitoring, redundancy management, probabilistic reliability theory, artificial intelligence for diagnostics and prognostics, and formal validation methods, but also "quasi-technical" techniques and disciplines such as quality assurance, systems architecture and engineering, knowledge capture, information fusion, testability and maintainability, and human factors. This groundbreaking book defines and explains this new discipline, providing frameworks and methodologies for implementation and further research. Each chapter includes experiments, numerical examples, simulations and case studies. It is the ideal guide to this crucial topic for professionals or researchers in aerospace systems, systems engineering, production engineering, and reliability engineering. Solves prognostic information selection and decision-level information fusion issues Presents integrated evaluation methodologies for complex aerospace system health conditions and software system reliability assessment Proposes a framework to perform fault diagnostics with a distributed intelligent agent system and a data mining approach for multistate systems Explains prognostic methods that combine both the qualitative system running state prognostics and the quantitative remaining useful life prediction


Book Synopsis Integrated System Health Management by : Jiuping Xu

Download or read book Integrated System Health Management written by Jiuping Xu and published by Academic Press. This book was released on 2017-05-18 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: ISHM is an innovative combination of technologies and methods that offers solutions to the reliability problems caused by increased complexities in design, manufacture, use conditions, and maintenance. Its key strength is in the successful integration of reliability (quantitative estimation of successful operation or failure), "diagnosibility" (ability to determine the fault source), and maintainability (how to maintain the performance of a system in operation). It draws on engineering issues such as advanced sensor monitoring, redundancy management, probabilistic reliability theory, artificial intelligence for diagnostics and prognostics, and formal validation methods, but also "quasi-technical" techniques and disciplines such as quality assurance, systems architecture and engineering, knowledge capture, information fusion, testability and maintainability, and human factors. This groundbreaking book defines and explains this new discipline, providing frameworks and methodologies for implementation and further research. Each chapter includes experiments, numerical examples, simulations and case studies. It is the ideal guide to this crucial topic for professionals or researchers in aerospace systems, systems engineering, production engineering, and reliability engineering. Solves prognostic information selection and decision-level information fusion issues Presents integrated evaluation methodologies for complex aerospace system health conditions and software system reliability assessment Proposes a framework to perform fault diagnostics with a distributed intelligent agent system and a data mining approach for multistate systems Explains prognostic methods that combine both the qualitative system running state prognostics and the quantitative remaining useful life prediction

Foundations and Practical Applications of Cognitive Systems and Information Processing

Foundations and Practical Applications of Cognitive Systems and Information Processing

Author: Fuchun Sun

Publisher: Springer Science & Business Media

Published: 2013-09-21

Total Pages: 869

ISBN-13: 3642378358

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"Foundations and Practical Applications of Cognitive Systems and Information Processing" presents selected papers from the First International Conference on Cognitive Systems and Information Processing, held in Beijing, China on December 15-17, 2012 (CSIP2012). The aim of this conference is to bring together experts from different fields of expertise to discuss the state-of-the-art in artificial cognitive systems and advanced information processing, and to present new findings and perspectives on future development. This book introduces multidisciplinary perspectives on the subject areas of Cognitive Systems and Information Processing, including cognitive sciences and technology, autonomous vehicles, cognitive psychology, cognitive metrics, information fusion, image/video understanding, brain-computer interfaces, visual cognitive processing, neural computation, bioinformatics, etc. The book will be beneficial for both researchers and practitioners in the fields of Cognitive Science, Computer Science and Cognitive Engineering. Fuchun Sun and Huaping Liu are both professors at the Department of Computer Science & Technology, Tsinghua University, China. Dr. Dewen Hu is a professor at the College of Mechatronics and Automation, National University of Defense Technology, Changsha, China.


Book Synopsis Foundations and Practical Applications of Cognitive Systems and Information Processing by : Fuchun Sun

Download or read book Foundations and Practical Applications of Cognitive Systems and Information Processing written by Fuchun Sun and published by Springer Science & Business Media. This book was released on 2013-09-21 with total page 869 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Foundations and Practical Applications of Cognitive Systems and Information Processing" presents selected papers from the First International Conference on Cognitive Systems and Information Processing, held in Beijing, China on December 15-17, 2012 (CSIP2012). The aim of this conference is to bring together experts from different fields of expertise to discuss the state-of-the-art in artificial cognitive systems and advanced information processing, and to present new findings and perspectives on future development. This book introduces multidisciplinary perspectives on the subject areas of Cognitive Systems and Information Processing, including cognitive sciences and technology, autonomous vehicles, cognitive psychology, cognitive metrics, information fusion, image/video understanding, brain-computer interfaces, visual cognitive processing, neural computation, bioinformatics, etc. The book will be beneficial for both researchers and practitioners in the fields of Cognitive Science, Computer Science and Cognitive Engineering. Fuchun Sun and Huaping Liu are both professors at the Department of Computer Science & Technology, Tsinghua University, China. Dr. Dewen Hu is a professor at the College of Mechatronics and Automation, National University of Defense Technology, Changsha, China.

AIAA Aerospace Sciences Meeting and Exhibit, 42nd

AIAA Aerospace Sciences Meeting and Exhibit, 42nd

Author:

Publisher:

Published: 2004

Total Pages: 532

ISBN-13:

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Book Synopsis AIAA Aerospace Sciences Meeting and Exhibit, 42nd by :

Download or read book AIAA Aerospace Sciences Meeting and Exhibit, 42nd written by and published by . This book was released on 2004 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Frontiers of Performability Engineering

Frontiers of Performability Engineering

Author: Durga Rao Karanki

Publisher: Springer Nature

Published:

Total Pages: 688

ISBN-13: 9819982588

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Book Synopsis Frontiers of Performability Engineering by : Durga Rao Karanki

Download or read book Frontiers of Performability Engineering written by Durga Rao Karanki and published by Springer Nature. This book was released on with total page 688 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Progress in Artificial Intelligence

Progress in Artificial Intelligence

Author: Luis Antunes

Publisher: Springer Science & Business Media

Published: 2011-10-05

Total Pages: 729

ISBN-13: 3642247687

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This book contains a selection of higher quality and reviewed papers of the 15th Portuguese Conference on Artificial Intelligence, EPIA 2011, held in Lisbon, Portugal, in October 2011. The 50 revised full papers presented were carefully reviewed and selected from a total of 203 submissions. The papers are organized in topical sections on affective computing, ambient intelligence environments, artificial intelligence methodologies for games, artificial intelligence in transportation systems, artificial life evolutionary algorithms, computational logic with applications, general artificial intelligence, intelligent robotics, knowledge discovery and business intelligence, multi-agent systems: theory and applications, social simulation and modeling, text mining and applications, and doctoral symposium on artificial intelligence.


Book Synopsis Progress in Artificial Intelligence by : Luis Antunes

Download or read book Progress in Artificial Intelligence written by Luis Antunes and published by Springer Science & Business Media. This book was released on 2011-10-05 with total page 729 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains a selection of higher quality and reviewed papers of the 15th Portuguese Conference on Artificial Intelligence, EPIA 2011, held in Lisbon, Portugal, in October 2011. The 50 revised full papers presented were carefully reviewed and selected from a total of 203 submissions. The papers are organized in topical sections on affective computing, ambient intelligence environments, artificial intelligence methodologies for games, artificial intelligence in transportation systems, artificial life evolutionary algorithms, computational logic with applications, general artificial intelligence, intelligent robotics, knowledge discovery and business intelligence, multi-agent systems: theory and applications, social simulation and modeling, text mining and applications, and doctoral symposium on artificial intelligence.

Prognostics and Health Management of Electronics

Prognostics and Health Management of Electronics

Author: Michael G. Pecht

Publisher: John Wiley & Sons

Published: 2008-09-11

Total Pages: 335

ISBN-13: 0470385839

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The first book on Prognostics and Health Management of Electronics Recently, the field of prognostics for electronic products has received increased attention due to the potential to provide early warning of system failures, forecast maintenance as needed, and reduce life cycle costs. In response to the subject's growing interest among industry, government, and academic professionals, this book provides a road map to the current challenges and opportunities for research and development in Prognostics and Health Management (PHM). The book begins with a review of PHM and the techniques being developed to enable a prognostics approach for electronic products and systems. building on this foundation, the book then presents the state of the art in sensor systems for in-situ health and usage monitoring. Next, it discusses the various models and algorithms that can be utilized in PHM. Finally, it concludes with a discussion of the opportunities in future research. Readers can use the information in this book to: Detect and isolate faults Reduce the occurrence of No Fault Found (NFF) Provide advanced warning of system failures Enable condition-based (predictive) maintenance Obtain knowledge of load history for future design, qualification, and root cause analysis Increase system availability through an extension of maintenance cycles and/or timely repair actions Subtract life cycle costs of equipment from reduction in inspection costs, down time, and inventory Prognostics and Health Management of Electronics is an indispensable reference for electrical engineers in manufacturing, systems maintenance, and management, as well as design engineers in all areas of electronics.


Book Synopsis Prognostics and Health Management of Electronics by : Michael G. Pecht

Download or read book Prognostics and Health Management of Electronics written by Michael G. Pecht and published by John Wiley & Sons. This book was released on 2008-09-11 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book on Prognostics and Health Management of Electronics Recently, the field of prognostics for electronic products has received increased attention due to the potential to provide early warning of system failures, forecast maintenance as needed, and reduce life cycle costs. In response to the subject's growing interest among industry, government, and academic professionals, this book provides a road map to the current challenges and opportunities for research and development in Prognostics and Health Management (PHM). The book begins with a review of PHM and the techniques being developed to enable a prognostics approach for electronic products and systems. building on this foundation, the book then presents the state of the art in sensor systems for in-situ health and usage monitoring. Next, it discusses the various models and algorithms that can be utilized in PHM. Finally, it concludes with a discussion of the opportunities in future research. Readers can use the information in this book to: Detect and isolate faults Reduce the occurrence of No Fault Found (NFF) Provide advanced warning of system failures Enable condition-based (predictive) maintenance Obtain knowledge of load history for future design, qualification, and root cause analysis Increase system availability through an extension of maintenance cycles and/or timely repair actions Subtract life cycle costs of equipment from reduction in inspection costs, down time, and inventory Prognostics and Health Management of Electronics is an indispensable reference for electrical engineers in manufacturing, systems maintenance, and management, as well as design engineers in all areas of electronics.