Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) - Tapa dura
Sinopsis
This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail.
The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making.
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De la contraportada
This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail.
The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making.
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Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering)
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Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering)
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Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering)
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Data-Driven Remaining Useful Life Prognosis Techniques
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Springer Berlin Heidelberg, 2017
ISBN 10: 3662540282
ISBN 13: 9783662540282
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Describes the basic data-driven remaining useful life prognosis theory systematically and in detailIncludes a wealth of degradation monitoring experiment data, practical prognosis methods, and various decision-making applications that employ progn. Nº de ref. del artículo: 132284892
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Data-Driven Remaining Useful Life Prognosis Techniques
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Springer Berlin Heidelberg, Springer Berlin Heidelberg Feb 2017, 2017
ISBN 10: 3662540282
ISBN 13: 9783662540282
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making. 448 pp. Englisch. Nº de ref. del artículo: 9783662540282
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Data-Driven Remaining Useful Life Prognosis Techniques | Stochastic Models, Methods and Applications
Impresión bajo demandaLibrería: preigu, Osnabrück, Alemania
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Buch. Condición: Neu. Data-Driven Remaining Useful Life Prognosis Techniques | Stochastic Models, Methods and Applications | Xiao-Sheng Si (u. a.) | Buch | xvii | Englisch | 2017 | Springer-Verlag GmbH | EAN 9783662540282 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Nº de ref. del artículo: 108396528
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Data-Driven Remaining Useful Life Prognosis Techniques : Stochastic Models, Methods and Applications
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Condición: New. Nº de ref. del artículo: 26376467523
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Data-Driven Remaining Useful Life Prognosis Techniques
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Springer Berlin Heidelberg, Springer Berlin Heidelberg Feb 2017, 2017
ISBN 10: 3662540282
ISBN 13: 9783662540282
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Buch. Condición: Neu. Neuware -This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail.The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 448 pp. Englisch. Nº de ref. del artículo: 9783662540282
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Data-Driven Remaining Useful Life Prognosis Techniques : Stochastic Models, Methods and Applications
Publicado por
Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2017
ISBN 10: 3662540282
ISBN 13: 9783662540282
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making. Nº de ref. del artículo: 9783662540282
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Data-Driven Remaining Useful Life Prognosis Techniques : Stochastic Models, Methods and Applications
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