000 03894nam a22005415i 4500
001 978-1-84996-320-6
003 DE-He213
005 20140220084516.0
007 cr nn 008mamaa
008 100730s2010 xxk| s |||| 0|eng d
020 _a9781849963206
_9978-1-84996-320-6
024 7 _a10.1007/978-1-84996-320-6
_2doi
050 4 _aTA169.7
050 4 _aT55-T55.3
050 4 _aTA403.6
072 7 _aTGPR
_2bicssc
072 7 _aTEC032000
_2bisacsh
082 0 4 _a658.56
_223
100 1 _aLisnianski, Anatoly.
_eauthor.
245 1 0 _aMulti-state System Reliability Analysis and Optimization for Engineers and Industrial Managers
_h[electronic resource] /
_cby Anatoly Lisnianski, Ilia Frenkel, Yi Ding.
264 1 _aLondon :
_bSpringer London,
_c2010.
300 _aXVI, 393p. 114 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aMulti-state Systems in Nature and in Engineering -- Modern Stochastic Process Methods for Multi-state System Reliability Assessment -- Statistical Analysis of Reliability Data for Multi-state Systems -- Universal Generating Function Method -- Combined Universal Generating Function and Stochastic Process Method -- Reliability-associated Cost Assessment and Management Decisions for Multi-state Systems -- Aging Multi-state Systems -- Fuzzy Multi-state System: General Definition and Reliability Assessment.
520 _aMulti-state System Reliability Analysis and Optimization for Engineers and Industrial Managers presents a comprehensive, up-to-date description of multi-state system (MSS) reliability as a natural extension of classical binary-state reliability. It presents all essential theoretical achievements in the field, but is also practically oriented. New theoretical issues are described, including: combined Markov and semi-Markov processes methods, and universal generating function techniques; statistical data processing for MSSs; reliability analysis of aging MSSs; methods for cost-reliability and cost-availability analysis of MSSs; and main definitions and concepts of fuzzy MSS. Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers also discusses life cycle cost analysis and practical optimal decision making for real world MSSs. Numerous examples are included in each section in order to illustrate mathematical tools. Besides these examples, real world MSSs (such as power generating and transmission systems, air-conditioning systems, production systems, etc.) are considered as case studies. Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers also describes basic concepts of MSS, MSS reliability measures and tools for MSS reliability assessment and optimization. It is a self-contained study resource and does not require prior knowledge from its readers, making the book attractive for researchers as well as for practical engineers and industrial managers.
650 0 _aEngineering.
650 0 _aDistribution (Probability theory).
650 0 _aEngineering economy.
650 0 _aSystem safety.
650 0 _aBusiness logistics.
650 1 4 _aEngineering.
650 2 4 _aQuality Control, Reliability, Safety and Risk.
650 2 4 _aProduction/Logistics.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aComputational Intelligence.
650 2 4 _aEngineering Economics, Organization, Logistics, Marketing.
700 1 _aFrenkel, Ilia.
_eauthor.
700 1 _aDing, Yi.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781849963190
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-84996-320-6
912 _aZDB-2-ENG
999 _c111015
_d111015