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 (Author), Zhang, Zheng-Xin (Author), Hu, Chang-Hua (Author)
Corporate Author: SpringerLink (Online service)
Format: e-Book
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2017.
Edition:1st ed. 2017.
Series:Springer Series in Reliability Engineering,
Subjects:
Security systems.
Probabilities.
Operations research.
Statistics .
Security Science and Technology.
Probability Theory.
Operations Research and Decision Theory.
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Online Access:Full-text access
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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.

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