Data-Driven Remaining Useful Life Prognosis Techniques : Stochastic Models, Methods and Applications /
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...
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Main Authors: | |
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Corporate Authors: | |
Group Author: | ; |
Published: |
Springer Berlin Heidelberg : Imprint: Springer,
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Publisher Address: | Berlin, Heidelberg : |
Publication Dates: | 2017. |
Literature type: | eBook |
Language: | English |
Series: |
Springer Series in Reliability Engineering,
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Subjects: | |
Online Access: |
http://dx.doi.org/10.1007/978-3-662-54030-5 |
Summary: |
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 |
Carrier Form: | 1 online resource(XVII,430pages): illustrations. |
ISBN: | 9783662540305 |
Index Number: | TA169 |
CLC: | TB114.3 |
Contents: | From the Contents: Part I Introduction, Basic Concepts and Preliminaries -- Overview -- Advances in Data-Driven Remaining Useful Life Prognosis -- Part II Remaining Useful Life Prognosis for Linear Stochastic Degrading Systems -- Part III Remaining Useful Life Prognosis for Nonlinear Stochastic Degrading Systems -- Part IV Applications of Prognostics in Decision Making -- Variable Cost-based Maintenance Model from Prognostic Information. |