Data-Driven Remaining Useful Life Prognosis Techniques - Stochastic Models, Methods and Applications (Hardcover, 1st ed. 2017)

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.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Springer Series in Reliability Engineering
Release date:	February 2017
First published:	2017
Authors:	Xiao-Sheng Si • Zheng-Xin Zhang • Changhua Hu
Dimensions:	235 x 155 x 25mm (L x W x T)
Format:	Hardcover
Pages:	430
Edition:	1st ed. 2017
ISBN-13:	978-3-662-54028-2
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Business & Economics > Business & management > Management & management techniques > Operational research Books > Science & Mathematics > Mathematics > Applied mathematics > Stochastics Books > Professional & Technical > Mechanical engineering & materials > Production engineering > Reliability engineering Promotions
LSN:	3-662-54028-2
Barcode:	9783662540282

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