Stochastic models for degradation-based reliability

Kharoufeh, Jeffrey P.; Cox, Steven M.

doi:10.1080/07408170590929009

Cited by 150 publications

(84 citation statements)

References 43 publications

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“…, λ ). Although the assumption of linear wear appears, on first glance, to be restrictive, nonlinear wear paths can be effectively approximated by piece-wise linear paths as demonstrated by Kharoufeh and Cox [14].…”

Section: Model Descriptionmentioning

confidence: 98%

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On a Markov‐modulated shock and wear process

Kharoufeh

Mixon

2009

Naval Research Logistics

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Abstract:We present transient and asymptotic reliability indices for a single-unit system that is subject to Markov-modulated shocks and wear. The transient results are derived from the (transform) solution of an integro-differential equation describing the joint distribution of the cumulative degradation process and the state of the modulating process. Additionally, we prove the asymptotic normality of a properly centered and time-scaled version of the cumulative degradation at time t. This asymptotic result leads to a simple normal approximation for a properly centered and space-scaled version of the system's lifetime distribution. Two numerical examples illustrate the quality of the normal approximation.

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Section: Model Descriptionmentioning

confidence: 98%

“…Specifically, we prove the asymptotic normality of these properly scaled quantities. The asymptotic results serve as simple approximations for the lifetime distribution and may be useful for degradation-based reliability models such as those described by Gebraeel et al [9] and Kharoufeh and Cox [14] among others.…”

Section: Introductionmentioning

confidence: 97%

On a Markov‐modulated shock and wear process

Kharoufeh

Mixon

2009

Naval Research Logistics

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“…Lu and Meeker (1993) first proposed the GPM, which shifts reliability analysis from failure time to failure mode analysis. Improvements to their seminal work are proposed by Girish et al (2003) who used neural networks to estimate the failure times for censored systems; Kharoufeh and Cox (2005) apply Markovian degradation models to estimate the failure time for censored systems; Chen and Zhang (2005) attempt to infer the lifetime distribution itself instead of the distribution parameters from the available data; and Xu and Zhao (2005) extend the approach to use multivariate degradation measures.…”

Section: Type-i Prognostic Models: Traditional Time-to-failure Analysismentioning

confidence: 99%

Lifecycle Prognostics Architecture for Selected High-Cost Active Components

Lybeck¹,

Pham²,

Tawfik³

et al. 2011

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There are an extensive body of knowledge and some commercial products available for calculating prognostics, remaining useful life, and damage index parameters. The application of these technologies within the nuclear power community is still in its infancy. Online monitoring and condition-based maintenance is seeing increasing acceptance and deployment, and these activities provide the technological bases for expanding to add predictive/prognostics capabilities. In looking to deploy prognostics there are three key aspects of systems that are presented and discussed: component/system/structure selection, prognostic algorithms, and prognostics architectures. Criteria are presented for component selection: feasibility, failure probability, consequences of failure, and benefits of the prognostics and health management (PHM) system. The basis and methods commonly used for prognostics algorithms are reviewed and summarized. Criteria for evaluating PHM architectures are presented: open, modular architecture; platform independence; graphical user interface for system development and/or results viewing; web-enabled tools; scalability; and standards compatibility.Thirteen software products were identified and discussed in the context of being potentially useful for deployment in a PHM program applied to systems in a nuclear power plant (NPP). These products were evaluated by using information available from company websites, product brochures, fact sheets, scholarly publications, and direct communication with vendors. The thirteen products were classified into four groups of software: research tools, PHM system development tools, deployable architectures, and peripheral tools. Eight software tools fell into the deployable architectures category. Of those eight, only two employ all six modules of a full PHM system. Five systems did not offer prognostic estimates, and one system employed the full health monitoring suite but lacked operations and maintenance support. Each product is briefly described in Appendix A, "Assessment Criteria." Selection of the most appropriate software package for a particular application will depend on the chosen component, system, or structure. Ongoing research will determine the most appropriate choices for a successful demonstration of PHM systems in aging NPPs.iv v SUMMARYAt the start of 2011, there were 439 nuclear power plants (NPPs) in the commercial global fleet; these plants started operation with 30-or 40-year licenses. The 104 NPPs in the United States represent about 10% of the installed capacity and currently provide about 20% of U.S. electricity. They have become highly efficient with an average capacity factor in excess of 91%, and they are a key element in the delivery of base-load electricity. They were originally licensed for 40 years, and a process has been established to enable license extension, to permit operation from 40 to 60 years. As of July 2011, license extensions have been granted for 71 plants at 40 sites. An additional 13 plants at nine sites are currently ...

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“…Kharoufeh [11] derived the explicit probability distribution of the random failure time for single-unit systems that deteriorate continuously and additively due to the influence of a random environment modeled as a general, finite-state Markov process. Kharoufeh & Cox [12] presented a degradation-based procedure for the estimation of full, and residue lifetime distribution for single-unit systems using real sensor data. Boulanger & Escobar [3], Tseng et al [27], Yu & Chiao [31], and Joseph & Yu [10] used experimental design to improve reliability for degradation processes.…”

Section: B Literature Review On Degradation Processesmentioning

confidence: 99%