Component Reliability Modeling Through the Use of Bayesian Networks and Applied Physics-based Models

Rabiei, Elaheh; White, Mark H.; Mosleh, Ali; Lyer, Subramanian; Woo, Jin Seok

doi:10.1109/ram.2018.8462986

Cited by 4 publications

(7 citation statements)

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“…The EAB equation as a function of time is used to obtain a probabilistic model represented by equations (11) and (15) quantifying the uncertainty of n shown in Figure 9. The uncertainty of n gives rise to the distribution of the expected lifespan of cable insulation shown in Figure 11.…”

Section: Discussionmentioning

confidence: 99%

“…Experimental data are plotted by the discrete patterns in Figures 6-8. Figure 6(c) is taken as an example to demonstrate how the uncertainty of n can be determined according to equations (9), (11), and (15). By the same token, the approach applied to Figure 6(c) can be applied to Figure 6(a), Figure 6(b), Figure 7, and Figure 8.…”

Section: Sirmentioning

confidence: 99%

“…The proposed model is validated by the experimental results of the EPR and SIR provided by a published report. 4 The uncertainty of the parameters in the model is assessed by Bayesian methods 14–20 reflecting the difference between model prediction and experimental data.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic model of degradation of cable insulations in nuclear power plants

Chang

Mosleh

2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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Possible degradation of cable insulations exposed to radiation and heat is a safety and operational concern for nuclear power plants, particularly in the context of a license extension for the operation beyond original 40-year design life. Ethylene propylene rubber and silicone rubber are two major materials for the cable insulation. Degradation decreases the elongation at break of the insulation, which may lead to the exposure of the metal core in the cable, causing potential safety issues. This article proposes a mechanistic predictive model for the elongation at break as a function of time, temperature, and radiation dose rate. In the proposed model, the elongation at break curve is divided into an incubation section and a drop-off section with two parameters. In contrast to traditional deterministic approaches, this model projects the expected lifespan of cable insulation in the form of a probability distribution. The article also provides a validation of the model behavior using published experimental data.

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Section: Discussionmentioning

confidence: 99%

Section: Sirmentioning

confidence: 99%

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Probabilistic model of degradation of cable insulations in nuclear power plants

Chang

Mosleh

2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…9 The shape and scale (characteristic life) parameters of the TDDB Weibull distribution are dependent on oxide-thickness and peak electric field, and for a particular technology can be extracted from literature. [10][11][12] The temperature dependence of TDDB follows the Arrhenius equation:…”

Section: Pof/circuit Simulationmentioning

confidence: 99%

“…The fitting temperature dependent activation energy value from equation (12) for the 180 nm technology node is 0.74 eV.…”

Section: Pof/circuit Simulationmentioning

confidence: 99%

Method and software platform for electronic COTS parts reliability estimation in space applications

Rabiei

Huang

Chien

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Adoption of electronic Commercial-Off-The-Shelf (COTS) parts in various industrial products is rapidly increasing due to the accessibility and appealing lower cost of these commodities. Depending on the type of application, having an accurate understanding of the COTS failure information can be crucial to ensure the reliability and safety of the final products. On the other hand, frequent large-scale testing is often cost prohibitive and time consuming for emerging technologies, especially in the consumer electronics sector where minimizing time-to-market and cost is critical. This paper presents a comprehensive Bayesian approach and software platform (named COTS Reliability Expert System), that integrates multiple pieces of heterogeneous information about the failure rate of COTS parts. The ultimate goal is to reduce dependency on testing for reliability analysis and yet to obtain a more accurate “order of magnitude” estimate of the failure rate through an efficient process. The method provides a foundation for incorporating manufacturers reliability data, estimates based on underlying physics-of-failure mechanisms and circuit simulations, partially relevant life test data of similar (but not necessarily identical) parts, and expert opinions on the manufacturing process of the COTS part of interest. The developed expert system uses Bayesian estimation to integrate all these types of evidence. The methodology is demonstrated in estimating the failure rate of a static random-access memory (SRAM) part.

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