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

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...

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Bibliographic Details
Main Authors: Si, Xiao-Sheng, Zhang, Zheng-Xin (Author), Hu, Chang-Hua (Author)
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 2017, 2017
Edition:1st ed. 2017
Series:Springer Series in Reliability Engineering
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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 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