Authors:
- Describes the basic data-driven remaining useful life prognosis theory systematically and in detail
- Includes a wealth of degradation monitoring experiment data, practical prognosis methods, and various decision-making applications that employ prognostic information
- Highlights new findings on remaining useful life prognosis techniques for linear/nonlinear systems
- Provides a complete picture of prognostic information-based decision-making applications
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Series in Reliability Engineering (RELIABILITY)
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Table of contents (16 chapters)
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Front Matter
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Introduction, Degradation Data Acquisition and Evaluation
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Front Matter
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Prognostic Techniques for Linear Degrading Systems
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Front Matter
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Prognostic Techniques for Nonlinear Degrading Systems
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Front Matter
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Applications of Prognostic Information
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Front Matter
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About this book
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.
Authors and Affiliations
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Department of Automation, Xi’an Institute of High-Technology, Xi’an, China
Xiao-Sheng Si, Chang-Hua Hu
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Department of Automation, Xi’an Institute of High-Technology Department of Automation, Xi’an, China
Zheng-Xin Zhang
Bibliographic Information
Book Title: Data-Driven Remaining Useful Life Prognosis Techniques
Book Subtitle: Stochastic Models, Methods and Applications
Authors: Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
Series Title: Springer Series in Reliability Engineering
DOI: https://doi.org/10.1007/978-3-662-54030-5
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: National Defense Industry Press and Springer-Verlag GmbH Germany 2017
Hardcover ISBN: 978-3-662-54028-2Published: 09 February 2017
Softcover ISBN: 978-3-662-57173-6Published: 13 July 2018
eBook ISBN: 978-3-662-54030-5Published: 20 January 2017
Series ISSN: 1614-7839
Series E-ISSN: 2196-999X
Edition Number: 1
Number of Pages: XVII, 430
Number of Illustrations: 20 b/w illustrations, 84 illustrations in colour
Additional Information: Jointly published with National Defense Industry Press, Beijing, China
Topics: Quality Control, Reliability, Safety and Risk, Probability Theory and Stochastic Processes, Operations Research/Decision Theory, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences