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Published by Taylor & Francis Ltd, 2024
ISBN 10: 1032562978 ISBN 13: 9781032562971
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Published by Taylor & Francis Ltd, 2023
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Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 1032562978 ISBN 13: 9781032562971
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Paperback. Condition: new. Paperback. The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and computer science.The bookDiscusses basic as well as advance research in the field of prognosticsExplores integration of data collection, fault detection, degradation modeling and reliability prediction in one volumeCovers prognostics and health management (PHM) of engineering systemsDiscusses latest approaches in the field of prognostics based on machine learningThe text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior, based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, manufacturing science, electrical engineering, and computer science. The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 1032562978 ISBN 13: 9781032562971
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Paperback. Condition: new. Paperback. The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and computer science.The bookDiscusses basic as well as advance research in the field of prognosticsExplores integration of data collection, fault detection, degradation modeling and reliability prediction in one volumeCovers prognostics and health management (PHM) of engineering systemsDiscusses latest approaches in the field of prognostics based on machine learningThe text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior, based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, manufacturing science, electrical engineering, and computer science. The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Language: English
Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 1032562978 ISBN 13: 9781032562971
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Paperback. Condition: new. Paperback. The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and computer science.The bookDiscusses basic as well as advance research in the field of prognosticsExplores integration of data collection, fault detection, degradation modeling and reliability prediction in one volumeCovers prognostics and health management (PHM) of engineering systemsDiscusses latest approaches in the field of prognostics based on machine learningThe text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior, based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, manufacturing science, electrical engineering, and computer science. The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Taschenbuch. Condition: Neu. Intelligent Prognostics for Engineering Systems with Machine Learning Techniques | Gunjan Soni (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | CRC Press | EAN 9781032562971 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering.
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and computer science.The bookDiscusses basic as well as advance research in the field of prognosticsExplores integration of data collection, fault detection, degradation modeling and reliability prediction in one volumeCovers prognostics and health management (PHM) of engineering systemsDiscusses latest approaches in the field of prognostics based on machine learningThe text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior, based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, manufacturing science, electrical engineering, and computer science. 262 pp. Englisch.