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Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 260 pages. 9.18x6.12x9.21 inches. In Stock.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Añadir al carritoHardcover. Condición: Brand New. 280 pages. 9.19x6.13x0.78 inches. In Stock.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2024
ISBN 10: 1032562978 ISBN 13: 9781032562971
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 82,35
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Añadir al carritoPaperback. Condición: 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.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2024
ISBN 10: 1032562978 ISBN 13: 9781032562971
Librería: CitiRetail, Stevenage, Reino Unido
EUR 90,32
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Añadir al carritoPaperback. Condición: 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.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2024
ISBN 10: 1032562978 ISBN 13: 9781032562971
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 110,57
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: 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.
Librería: preigu, Osnabrück, Alemania
EUR 91,90
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Añadir al carritoTaschenbuch. Condición: 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.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 108,11
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Añadir al carritoTaschenbuch. Condición: 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.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 164,54
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Añadir al carritoCondición: New. PRINT ON DEMAND.
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
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Añadir al carritoHRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
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Añadir al carritoHRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 155,20
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Añadir al carritoBuch. Condición: 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.
Librería: moluna, Greven, Alemania
EUR 140,83
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Gunjan Soni did his B.E. from University of Rajasthan, M.Tech from IIT, Delhi and PhD. From Birla Institute of Technology and Science, Pilani in 2012. He is presently working as an Assistant Professor with Department of Mechanical Engineering, Malaviya N.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 171,54
Cantidad disponible: 1 disponibles
Añadir al carritoBuch. Condición: 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, 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.