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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
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:
Διαθέσιμο Online:Full Text via HEAL-Link
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024 7 |a 10.1007/978-3-662-54030-5  |2 doi 
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100 1 |a Si, Xiao-Sheng.  |e author. 
245 1 0 |a Data-Driven Remaining Useful Life Prognosis Techniques  |h [electronic resource] :  |b Stochastic Models, Methods and Applications /  |c by Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2017. 
300 |a XVII, 430 p. 104 illus., 84 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Springer Series in Reliability Engineering,  |x 1614-7839 
505 0 |a 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. 
520 |a 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. 
650 0 |a Engineering. 
650 0 |a Operations research. 
650 0 |a Decision making. 
650 0 |a Probabilities. 
650 0 |a Statistics. 
650 0 |a Quality control. 
650 0 |a Reliability. 
650 0 |a Industrial safety. 
650 1 4 |a Engineering. 
650 2 4 |a Quality Control, Reliability, Safety and Risk. 
650 2 4 |a Probability Theory and Stochastic Processes. 
650 2 4 |a Operation Research/Decision Theory. 
650 2 4 |a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 
700 1 |a Zhang, Zheng-Xin.  |e author. 
700 1 |a Hu, Chang-Hua.  |e author. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783662540282 
830 0 |a Springer Series in Reliability Engineering,  |x 1614-7839 
856 4 0 |u http://dx.doi.org/10.1007/978-3-662-54030-5  |z Full Text via HEAL-Link 
912 |a ZDB-2-ENG 
950 |a Engineering (Springer-11647)