The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
A Srividya - One of the best experts on this subject based on the ideXlab platform.
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Test Interval optimization of safety systems of nuclear power plant using fuzzy genetic approach
Reliability Engineering & System Safety, 2007Co-Authors: V Gopika, H S Kushwaha, Ajit K Verma, A SrividyaAbstract:Abstract Probabilistic safety assessment (PSA) is the most effective and efficient tool for safety and risk management in nuclear power plants (NPP). PSA studies not only evaluate risk/safety of systems but also their results are very useful in safe, economical and effective design and operation of NPPs. The latter application is popularly known as “Risk-Informed Decision Making”. Evaluation of technical specifications is one such important application of Risk-Informed decision making. Deciding Test Interval (TI), one of the important technical specifications, with the given resources and risk effectiveness is an optimization problem. Uncertainty is inherently present in the availability parameters such as failure rate and repair time due to the limitation in assessing these parameters precisely. This paper presents a solution to Test Interval optimization problem with uncertain parameters in the model with fuzzy-genetic approach along with a case of application from a safety system of Indian pressurized heavy water reactor (PHWR).
Jin Jiang - One of the best experts on this subject based on the ideXlab platform.
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analysis of surveillance Test Interval by markov process for sds1 in candu nuclear power plants
Reliability Engineering & System Safety, 2008Co-Authors: Jin JiangAbstract:The Canadian Nuclear Safety Commission (CNSC) requires that each shutdown system (SDS) of CANDU plant should be available more than 99.9% of the reactor operating time and be Tested periodically. The compliance with the availability requirement should be demonstrated using the component failure rate data and the benefits of the Tests. There are many factors that should be considered in determining the surveillance Test Interval (STI) for the SDSs. These includes: the desired target availability, the actual unavailability, the probability of spurious trips, the Test duration, and the side effects such as wear-out, human errors, and economic burdens. A Markov process model is developed to study the effect of Test Interval in the shutdown system number one (SDS1) in this paper. The model can provide the quantitative data required for selecting the STI. Representing the state transitions in the SDS1 by a time-homogeneous Markov process, the model can be used to quantify the effect of surveillance Test durations and Interval on the unavailability and the spurious trip probability. The model can also be used to analyze the variation of the core damage probability with respect to changes in the Test Interval once combined with the conditional core damage model derived from the event trees and the fault trees of probabilistic safety assessment (PSA) of the nuclear power plant (NPP).
Kholoud Porter - One of the best experts on this subject based on the ideXlab platform.
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CD4 decline in seroconverter and seroprevalent individuals in the precombination of antiretroviral therapy era.
AIDS (London England), 2010Co-Authors: S. Lodi, Giota Touloumi, Nikos Pantazis, Gilles Chene, AG Babiker, Andrew Phillips, Philippe Vanhems, Heiner C Bucher, Kholoud PorterAbstract:BACKGROUND: Studies based on seroconverters have increased our understanding of HIV disease. It is not clear, however, whether their disease progression differs from that of the general HIV population, given their reasons for presenting for Testing. METHODS: Using linear mixed models we compared CD4 decline rates for a seroconverter (CASCADE) and seroprevalent group (Concorde trial) with time origin being dates of seroconversion and randomization, respectively. Follow-up was censored at the earlier of last alive date and 1 January 1996. Analyses were adjusted for risk group, age and sex. To explore the role of symptomatic seroconversion we further categorized seroconverters into two groups: with and without an HIV Test Interval below 30 days as proxy. RESULTS: The 7226 seroconverter and 1746 seroprevalent eligible individuals were mainly men (78 and 85%, respectively) infected through sex between men (52 and 63%) with mean [95% confidence Interval (CI)] baseline CD4 cell count of 610 (602, 619) and 492 (479, 505) cells/μl, respectively. There was no evidence that rate of CD4 decline differs between the two groups even after adjusting for potential confounders (P = 0.67). Estimated loss in the year after reaching an arbitrary threshold of 400 cells/μl was 67 (95% CI 65, 69) and 67 (64, 69) cells/μl in the seroconverter and seroprevalent group, respectively. Whereas seroconverters with Test Interval below 30 days (n = 310) experienced faster decline, there was no difference in rates between other seroconverters and seroprevalent individuals (P = 0.87). CONCLUSIONS: These data suggest that estimates of HIV progression derived from seroconverters are likely to hold more generally for the HIV-infected population.
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the relationship between hiv seroconversion illness hiv Test Interval and time to aids in a seroconverter cohort
Epidemiology and Infection, 2003Co-Authors: F Tyrer, A S Walker, James W Gillett, Kholoud PorterAbstract:Seroconversion illness is known to be associated with more rapid HIV disease progression. However, symptoms are often subjective and prone to recall bias. We describe symptoms reported as seroconversion illness and examine the relationship between illness, HIV Test Interval (time between antibody-negative and anibody-positive Test dates) and the effect of both on time to AIDS from seroconversion. We used a Cox model, adjusting for age, sex, exposure group and year of estimated seroconversion. Of 1820 individuals, information on seroconversion illness was available for 1244 of whom 423 (34%) reported symptomatic seroconversion. Persons with a short Test Interval (< or = 2 months) were significantly more likely to report an illness than people with a longer Interval (OR 6.76, 95% CI 4.75-9.62). Time to AIDS was significantly faster (P = 0.01) in those with a short Test Interval. The HIV Test Interval is a useful replacement for information on seroconversion illness in studies of HIV disease progression.
Jayantha P. Liyanage - One of the best experts on this subject based on the ideXlab platform.
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IEEM - Application of Prognostics and Health Management to Low Demand Systems: Use of Condition Data to Help Determine Function Test Interval
2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2018Co-Authors: Jayantha P. LiyanageAbstract:Many safety instrumented systems (SIS) such as emergency shut down (ESD) are designed as low demand systems, whose functionalities are only triggered under certain conditions once or fewer times a year. Reliability levels of several critical low demand systems were observed to be high on the Norwegian continental shelf (NCS) over the last 15 years [1]. In general terms, there is a lack of confidence in whether the functionality of a low demand system can be initiated and how well the system can perform upon a real demand. This paper studies such challenges and suggests the application of prognostics and health management (PHM) to evaluate the function Test Interval instead of remaining useful lifetime. The value of condition data is justified and reflected in the estimation of failure rate and function Test Interval. The application of PHM to low demand systems can help enhance a company’s confidence in system availability and operational reliability and its adherence to a predictive maintenance practice.
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IEEM - Low Demand Safety Instrumented System: Update of Function Test Intervals with Layer of Protection Analysis in the Operational Phase
2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2018Co-Authors: Jayantha P. LiyanageAbstract:In the oil & gas industry, the estimation and updating of function Test Intervals of critical safety instrumented systems (SIS) are critical for safe production. The definition of the new Test Interval of a SIS is closely related to its failure rate and average probability of failure on demand (PFD). In current practice, the evaluation process is employed on each individual SIS, and the overall safety level is usually not justified. When such analyses are performed on multiple safety instrumented protection layers, there is a tendency for the overall safety level of the protection system to be increased to a very high level, i.e. overprotection. This paper aims to suggest an alternative approach to estimating Test Intervals, with a focus on overall safety level. The results show the potential to extend function Test Intervals in a more ambitious way, without degrading the safety level of the overall protection system or each specific SIS.
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Low Demand Safety Instrumented System: Update of Function Test Intervals with Layer of Protection Analysis in the Operational Phase
2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2018Co-Authors: Jayantha P. LiyanageAbstract:In the oil & gas industry, the estimation and updating of function Test Intervals of critical safety instrumented systems (SIS) are critical for safe production. The definition of the new Test Interval of a SIS is closely related to its failure rate and average probability of failure on demand (PFD). In current practice, the evaluation process is employed on each individual SIS, and the overall safety level is usually not justified. When such analyses are performed on multiple safety instrumented protection layers, there is a tendency for the overall safety level of the protection system to be increased to a very high level, i.e. overprotection. This paper aims to suggest an alternative approach to estimating Test Intervals, with a focus on overall safety level. The results show the potential to extend function Test Intervals in a more ambitious way, without degrading the safety level of the overall protection system or each specific SIS.
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Application of Prognostics and Health Management to Low Demand Systems: Use of Condition Data to Help Determine Function Test Interval
2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2018Co-Authors: Jayantha P. LiyanageAbstract:Many safety instrumented systems (SIS) such as emergency shut down (ESD) are designed as low demand systems, whose functionalities are only triggered under certain conditions once or fewer times a year. Reliability levels of several critical low demand systems were observed to be high on the Norwegian continental shelf (NCS) over the last 15 years [1]. In general terms, there is a lack of confidence in whether the functionality of a low demand system can be initiated and how well the system can perform upon a real demand. This paper studies such challenges and suggests the application of prognostics and health management (PHM) to evaluate the function Test Interval instead of remaining useful lifetime. The value of condition data is justified and reflected in the estimation of failure rate and function Test Interval. The application of PHM to low demand systems can help enhance a company's confidence in system availability and operational reliability and its adherence to a predictive maintenance practice.
Whitney Kimble - One of the best experts on this subject based on the ideXlab platform.
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long term maintenance of immediate or delayed extinction is determined by the extinction Test Interval
Learning & Memory, 2010Co-Authors: Justin S Johnson, Martha Escobar, Whitney KimbleAbstract:Short acquisition-extinction Intervals (immediate extinction) can lead to either more or less spontaneous recovery than long acquisition-extinction Intervals (delayed extinction). Using rat subjects, we observed less spontaneous recovery following immediate than delayed extinction (Experiment 1). However, this was the case only if a relatively long extinction-Test Interval was used; a relatively short extinction-Test Interval yielded the opposite result (Experiment 2). Previous data appear consistent with this observation suggesting that, although delayed extinction appears more beneficial in the short term, immediate extinction may have more favorable long-term effects. These observations may have important implications for attenuation of relapse in clinical situations.
