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Data-Driven Remaining Useful Life Prognosis Techniques
Title statement Data-Driven Remaining Useful Life Prognosis Techniques [electronic resource] : Stochastic Models, Methods and Applications / by Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu. Publication Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2017. Phys.des. XVII, 430 p. 104 illus., 84 illus. in color. online resource. ISBN 9783662540305 Edition Springer Series in Reliability Engineering, ISSN 1614-7839 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. Notes to Availability Přístup pouze pro oprávněné uživatele Another responsib. Zhang, Zheng-Xin. Hu, Chang-Hua. Another responsib. SpringerLink (Online service) Subj. Headings Engineering. * Operations research. * Decision making. * Probabilities. * Statistics. * Quality control. * Reliability. * Industrial safety. Form, Genre elektronické knihy electronic books Country Německo Language angličtina Document kind Electronic books URL Plný text pro studenty a zaměstnance UPOL book
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.
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.
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