By Ilia B. Frenkel, Alex Karagrigoriou, Anatoly Lisnianski, Andre V. Kleyner
"This e-book offers the most recent advancements within the box of reliability technology targeting utilized reliability, probabilistic versions and hazard research. It presents readers with the main updated advancements during this box and consolidates examine actions in different parts of utilized reliability engineering. The e-book is timed to commemorate Boris Gnedenko's centennial via bringing jointly leading researchers, scientists, and practitioners within the box of Prof. Gnednko's services. The creation, written through Prof. Igor Ushakov, a private pal and a colleague of Boris Gnedenko, explains the numerous effect and contribution Gnedenko's paintings made at the reliability concept and the trendy reliability perform. The booklet covers traditional and modern (recently emerged) themes in reliability technological know-how, that have obvious prolonged study actions within the fresh years. those subject matters contain: degradation research and multi-state process reliability; networks and massive scale platforms; upkeep types; statistical inference in reliability, and; physics of mess ups and reliability demonstration. All of those issues current an exceptional curiosity to researchers and practitioners, having been commonly researched long ago years and lined at a good number of overseas meetings and in a large number of magazine articles. This e-book pulls jointly this data with a coherent circulate of chapters, and is written by way of the lead scientists, researchers and practitioners of their respective fields. Logically divided into 5 sections, each one comprises numerous chapters protecting theoretical and useful matters, whereas case stories help the themes lower than discussion"-- Read more...
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Additional info for Applied reliability engineering and risk analysis : probabilistic models and statistical inference
Michel. 1997. Linear Systems. New York: McGraw-Hill. , P. Buchholz, and A. Panchenko. 2010. On the numerical analysis of inhomogeneous continuous-time Markov chains. INFORMS Journal on Computing 22 (3): 416–432. , C. Guedes Soares, M. Marseguerra, and E. Zio. 2002. Simulation modelling of repairable multicomponent deteriorating systems for ‘on condition’ maintenance optimisation. Reliability Engineering & System Safety 76: 255–264. T. R. Brailsford. 2005. A semi-Markov approach for modelling asset deterioration.
The usefulness of each approach depends on the structure of the failure modes and the condition monitoring indicators used to monitor the health status of a particular device. With respect to the CM indicators used to monitor failure modes, devices can be categorized into two cases with: (1) multiple independent failure modes 22 Applied Reliability Engineering and Risk Analysis and independent CM indicators; and (2) multiple independent failure modes and dependent CM indicators. Approaches I and II are related to these two cases.
Computers and Industrial Engineering 57: 298–303. E. G. Tu. 1986. Monte Carlo reliability modeling by inhomogeneous Markov processes. Reliability Engineering 16: 277–296. Li, W J. and H. Pham. 2005. Reliability modeling of multi-state degraded systems with multi-competing failures and random shocks. IEEE Transactions on Reliability 54 (2): 297–303. , E. H. Lin. 2012. A multistate physics model of component degradation based on stochastic petri nets and simulation. IEEE Transactions on Reliability 61 (4): 921–931.
Applied reliability engineering and risk analysis : probabilistic models and statistical inference by Ilia B. Frenkel, Alex Karagrigoriou, Anatoly Lisnianski, Andre V. Kleyner