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

by Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu

Overview

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 shastic 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 shastic 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 shastic degrading systems, nonlinear degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making.

Product Details

ISBN-13:	9783662571736
Publisher:	Springer Berlin Heidelberg
Publication date:	07/13/2018
Series:	Springer Series in Reliability Engineering
Edition description:	Softcover reprint of the original 1st ed. 2017
Pages:	430
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of 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 Shastic Degrading Systems.- Part III Remaining Useful Life Prognosis for Nonlinear Shastic Degrading Systems.- Part IV Applications of Prognostics in Decision Making.- Variable Cost-based Maintenance Model from Prognostic Information.