國立虎尾科技大學 |

Data-driven remaining useful life prognosis techniques = stochastic models, methods and applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Data-driven remaining useful life prognosis techniques/ by Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu.
Reminder of title:	stochastic models, methods and applications /
Author:	Si, Xiao-Sheng.
other author:	Zhang, Zheng-Xin.
Published:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2017.,
Description:	xvii, 430 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Service life (Engineering) - Mathematical models. -
Online resource:	http://dx.doi.org/10.1007/978-3-662-54030-5
ISBN:	9783662540305

Data-driven remaining useful life prognosis techniques = stochastic models, methods and applications /
Si, Xiao-Sheng.

Data-driven remaining useful life prognosis techniquesstochastic models, methods and applications /[electronic resource] :by Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu. - Berlin, Heidelberg :Springer Berlin Heidelberg :2017. - xvii, 430 p. :ill., digital ;24 cm. - Springer series in reliability engineering,1614-7839. - Springer series in reliability engineering..

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.

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.

ISBN: 9783662540305

Standard No.: 10.1007/978-3-662-54030-5doiSubjects--Topical Terms:

792591
Service life (Engineering)
--Mathematical models.

LC Class. No.: TA409.2

Dewey Class. No.: 658.5

Data-driven remaining useful life prognosis techniques = stochastic models, methods and applications /
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505 0 $a 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.
520 $a 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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