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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Detaylı Bibliyografya
Asıl Yazarlar: Si, Xiao-Sheng (Yazar), Zhang, Zheng-Xin (Yazar), Hu, Chang-Hua (Yazar)
Müşterek Yazar: SpringerLink (Online service)
Materyal Türü: e-Kitap
Dil:İngilizce
Baskı/Yayın Bilgisi: Berlin, Heidelberg : Springer Berlin Heidelberg : 2017.
Imprint: Springer,
Edisyon:1st ed. 2017.
Seri Bilgileri:Springer Series in Reliability Engineering,
Konular:
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 Erişim:Full-text access
Diğer Bilgiler
Özet: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.
Fiziksel Özellikler:XVII, 430 p. 104 illus., 84 illus. in color. online resource.
ISBN:9783662540305
ISSN:2196-999X
DOI:10.1007/978-3-662-54030-5

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