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

• Κύριοι συγγραφείς: Si, Xiao-Sheng (Συγγραφέας), Zhang, Zheng-Xin (Συγγραφέας), Hu, Chang-Hua (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2017.
• Σειρά: Springer Series in Reliability Engineering,
• Θέματα: Engineering.; Operations research.; Decision making.; Probabilities.; Statistics.; Quality control.; Reliability.; Industrial safety.; Quality Control, Reliability, Safety and Risk.; Probability Theory and Stochastic Processes.; Operation Research/Decision Theory.; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

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...

Πίνακας περιεχομένων:

• 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.