Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering)

Si, Xiao-Sheng, Zhang, Zheng-Xin, Hu, Chang-Hua

ISBN 10: 3662571730 / ISBN 13: 9783662571736

Edité par Springer, 2018

A propos de cet article

New.

N° de réf. du vendeur ERICA79636625717306

Détails bibliographiques

• Titre: Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering)
• Éditeur: Springer
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 3662571730
• ISBN à 13 chiffres: 9783662571736
• Reliure: Paperback
• État de l'article: New
• Type de livre: book

A propos de ce titre

Synopsis

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.