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Applied Reliability Engineering and Risk Analysis

dc.contributor.authorFrenkel, Ilia B.en
dc.contributor.authorKaragrigoriou, Alexen
dc.contributor.authorLisnianski, Anatolyen
dc.contributor.authorKleyner, Andre V.en
dc.creatorFrenkel, Ilia B.en
dc.creatorKaragrigoriou, Alexen
dc.creatorLisnianski, Anatolyen
dc.creatorKleyner, Andre V.en
dc.date.accessioned2019-12-02T10:35:11Z
dc.date.available2019-12-02T10:35:11Z
dc.date.issued2013
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/56835
dc.description.abstractThis book presents the latest developments in the field of reliability science focusing on applied reliability, probabilistic models and risk analysis.  It provides readers with the most up-to-date developments in this field and consolidates research activities in several areas of applied reliability engineering. The publication is timed to commemorate Boris Gnedenko's centennial by bringing together leading researchers, scientists, and practitioners in the field of Prof. Gnednko's expertise.  The Introduction, written by Prof. Igor Ushakov, a personal friend and a colleague of Boris Gnedenkoen
dc.sourceQuality and Reliability Engineering Seriesen
dc.source.urihttp://gbv.eblib.com/patron/FullRecord.aspx?p=1360807
dc.source.urihttp://ebooks.ciando.com/book/index.cfm/bok_id/982203
dc.source.urihttp://www.ciando.com/img/books/width167/1118701895_k.jpg
dc.source.urihttp://www.ciando.com/pictures/bib/1118701895bib_t_1.jpg
dc.subjectElectronic booksen
dc.titleApplied Reliability Engineering and Risk Analysisen
dc.typeinfo:eu-repo/semantics/article
dc.author.facultyΣχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Μαθηματικών και Στατιστικής / Department of Mathematics and Statistics
dc.type.uhtypeArticleen
dc.description.notes<p>Wirtschaft # Qualitätsmanagementciandode
dc.description.notesWirtschaft # Managementciando</p>de
dc.description.notes<p>ID: 874en
dc.description.notesElektronische Ressource Probabilistic Models and Statistical Inferenceen
dc.description.notesOnline-Ressource (451 pen
dc.description.noteseBooken
dc.description.notesDescription based upon print version of recorden
dc.description.notesCoveren
dc.description.notesTitle Pageen
dc.description.notesCopyrighten
dc.description.notesContentsen
dc.description.notesRemembering Boris Gnedenkoen
dc.description.notesList of Contributorsen
dc.description.notesPrefaceen
dc.description.notesAcknowledgementsen
dc.description.notesPart I Degradation Analysis, Multi-State and Continuous-State System Reliabilityen
dc.description.notesChapter 1 Methods of Solutions of Inhomogeneous Continuous Time Markov Chains for Degradation Process Modelingen
dc.description.notes1.1 Introductionen
dc.description.notes1.2 Formalism of ICTMCen
dc.description.notes1.3 Numerical Solution Techniquesen
dc.description.notes1.3.1 The Runge-Kutta Methoden
dc.description.notes1.3.2 Uniformizationen
dc.description.notes1.3.3 Monte Carlo Simulationen
dc.description.notes1.3.4 State-Space Enrichmenten
dc.description.notes1.4 Examplesen
dc.description.notes1.4.1 Example of Computing System Degradation.en
dc.description.notes1.4.2 Example of Nuclear Component Degradation1.5 Comparisons of the Methods and Guidelines of Utilizationen
dc.description.notes1.6 Conclusionen
dc.description.notesReferencesen
dc.description.notesChapter 2 Multistate Degradation and Condition Monitoring for Devices with Multiple Independent Failure Modesen
dc.description.notes2.1 Introductionen
dc.description.notes2.2 Multistate Degradation and Multiple Independent Failure Modesen
dc.description.notes2.2.1 Notationen
dc.description.notes2.2.2 Assumptionsen
dc.description.notes2.2.3 The Stochastic Process Modelen
dc.description.notes2.3 Parameter Estimationen
dc.description.notes2.4 Important Reliability Measures of a Condition-Monitored Deviceen
dc.description.notes2.5 Numerical Exampleen
dc.description.notes2.6 Conclusionen
dc.description.notesAcknowledgementsen
dc.description.notesReferences.en
dc.description.notesChapter 3 Time Series Regression with Exponential Errors for Accelerated Testing and Degradation Tracking3.1 Introductionen
dc.description.notes3.2 Preliminaries: Statement of the Problemen
dc.description.notes3.2.1 Relevance to Accelerated Testing, Degradation and Risken
dc.description.notes3.3 Estimation and Prediction by Least Squaresen
dc.description.notes3.4 Estimation and Prediction by MLEen
dc.description.notes3.4.1 Properties of the Maximum Likelihood Estimatoren
dc.description.notes3.5 The Bayesian Approach: The Predictive Distributionen
dc.description.notes3.5.1 The Predictive Distribution of YT+1 when λ > Aen
dc.description.notes3.5.2 The Predictive Distribution of YT+1 when λ ≤ Aen
dc.description.notes3.5.3 Alternative Prior for βen
dc.description.notesAcknowledgementsen
dc.description.notesReferences.en
dc.description.notesChapter 4 Inverse Lz-Transform for a Discrete-State Continuous-Time Markov Process and Its Application to Multi-State System Reliability Analysis4.1 Introductionen
dc.description.notes4.2 Inverse Lz-Transform: Definitions and Computational Procedureen
dc.description.notes4.2.1 Definitionsen
dc.description.notes4.2.2 Computational Procedureen
dc.description.notes4.3 Application of Inverse Lz-Transform to MSS Reliability Analysisen
dc.description.notes4.4 Numerical Exampleen
dc.description.notes4.5 Conclusionen
dc.description.notesReferencesen
dc.description.notesChapter 5 On the Lz-Transform Application for Availability Assessment of an Aging Multi-State Water Cooling System for Medical Equipmenten
dc.description.notes5.1 Introduction.en
dc.description.notes5.2 Brief Description of the Lz-Transform Method5.3 Multi-state Model of the Water Cooling System for the MRI Equipmenten
dc.description.notes5.3.1 System Descriptionen
dc.description.notes5.3.2 The Chiller Sub-Systemen
dc.description.notes5.3.3 The Heat Exchanger Sub-Systemen
dc.description.notes5.3.4 The Pump Sub-Systemen
dc.description.notes5.3.5 The Electric Board Sub-Systemen
dc.description.notes5.3.6 Model of Stochastic Demanden
dc.description.notes5.3.7 Multi-State Model for the MRI Cooling Systemen
dc.description.notes5.4 Availability Calculationen
dc.description.notes5.5 Conclusionen
dc.description.notesAcknowledgmentsen
dc.description.notesReferencesen
dc.description.notesChapter 6 Combined Clustering and Lz-Transform Technique to Reduce the Computational Complexity of a Multi-State System Reliability Evaluationen
dc.description.notes6.1 Introduction.en
dc.description.notes6.2 The Lz-Transform for Dynamic Reliability Evaluation for MSS.</p>en
dc.contributor.orcidKaragrigoriou, Alex [0000-0002-4919-2133]
dc.gnosis.orcid0000-0002-4919-2133


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