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170301 ||| eng |
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|a 9783662540305
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| 100 |
1 |
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|a Si, Xiao-Sheng
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| 245 |
0 |
0 |
|a Data-Driven Remaining Useful Life Prognosis Techniques
|h Elektronische Ressource
|b Stochastic Models, Methods and Applications
|c by Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
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| 250 |
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|a 1st ed. 2017
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 2017, 2017
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| 300 |
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|a XVII, 430 p. 104 illus., 84 illus. in color
|b online resource
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| 505 |
0 |
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|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
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| 653 |
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|a Security Science and Technology
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| 653 |
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|a Operations research
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| 653 |
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|a Statistics
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| 653 |
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|a Probability Theory
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| 653 |
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|a Security systems
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| 653 |
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|a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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| 653 |
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|a Operations Research and Decision Theory
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| 653 |
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|a Probabilities
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| 700 |
1 |
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|a Zhang, Zheng-Xin
|e [author]
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| 700 |
1 |
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|a Hu, Chang-Hua
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Springer Series in Reliability Engineering
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| 028 |
5 |
0 |
|a 10.1007/978-3-662-54030-5
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-662-54030-5?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 621
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| 520 |
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|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
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