000 03939nam a22005895i 4500
001 978-3-662-54030-5
003 DE-He213
005 20220801221746.0
007 cr nn 008mamaa
008 170201s2017 gw | s |||| 0|eng d
020 _a9783662540305
_9978-3-662-54030-5
024 7 _a10.1007/978-3-662-54030-5
_2doi
050 4 _aTH9701-9745
072 7 _aTNKS
_2bicssc
072 7 _aTEC032000
_2bisacsh
072 7 _aTNKS
_2thema
082 0 4 _a621
_223
100 1 _aSi, Xiao-Sheng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_957821
245 1 0 _aData-Driven Remaining Useful Life Prognosis Techniques
_h[electronic resource] :
_bStochastic Models, Methods and Applications /
_cby Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu.
250 _a1st ed. 2017.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2017.
300 _aXVII, 430 p. 104 illus., 84 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series in Reliability Engineering,
_x2196-999X
505 0 _aFrom 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.
520 _aThis 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.
650 0 _aSecurity systems.
_931879
650 0 _aProbabilities.
_94604
650 0 _aOperations research.
_912218
650 0 _aStatistics .
_931616
650 1 4 _aSecurity Science and Technology.
_931884
650 2 4 _aProbability Theory.
_917950
650 2 4 _aOperations Research and Decision Theory.
_931599
650 2 4 _aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
_931790
700 1 _aZhang, Zheng-Xin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_957822
700 1 _aHu, Chang-Hua.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_957823
710 2 _aSpringerLink (Online service)
_957824
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783662540282
776 0 8 _iPrinted edition:
_z9783662540299
776 0 8 _iPrinted edition:
_z9783662571736
830 0 _aSpringer Series in Reliability Engineering,
_x2196-999X
_957825
856 4 0 _uhttps://doi.org/10.1007/978-3-662-54030-5
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80022
_d80022