:

product description page

Data-driven Remaining Useful Life Prognosis Techniques : Stochastic Models, Methods and Applications

Data-driven Remaining Useful Life Prognosis Techniques : Stochastic Models, Methods and Applications - image 1 of 1

About this item

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.

Genre: Technology, Mathematics, Business + Money Management
Series Title: Springer Series in Reliability Engineering
Format: Hardcover
Publisher: Springer Verlag
Author: Xiao-sheng Si & Zheng-xin Zhang & Chang-hua Hu
Language: English
Street Date: March 6, 2017
TCIN: 52000261
UPC: 9783662540282
Item Number (DPCI): 248-39-3233
If the item details above aren’t accurate or complete, we want to know about it. Report incorrect product info.

Guest reviews

Prices, promotions, styles and availability may vary by store & online. See our price match guarantee. See how a store is chosen for you.