Data-Driven Remaining Useful Life Prognosis Techniques

Stochastic Models, Methods and Applications

Nonfiction, Science & Nature, Technology, Quality Control, Mathematics, Statistics
Cover of the book Data-Driven Remaining Useful Life Prognosis Techniques by Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu, Springer Berlin Heidelberg
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Author: Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu ISBN: 9783662540305
Publisher: Springer Berlin Heidelberg Publication: January 20, 2017
Imprint: Springer Language: English
Author: Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
ISBN: 9783662540305
Publisher: Springer Berlin Heidelberg
Publication: January 20, 2017
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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