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Probability assessment reliability predictions

Assessments of the probability of operating for a period of time without safety failures are formally reliability predictions (reliability with respect to that subset of possible failures). In our mathematical treatment, we assume a system for which the reliability parameter is a probability of failure per demand (pfd) our approach can easily be extended to systems for which the reliability model is an exponential, continuous-time model, with a failure rate as its parameter. [Pg.108]

Limit state analysis is readily applicable to failure conditions, which occur when the demand imposed on the component, or system exceeds its capability. The probability of failure is the probability that the limit state functions are violated. These probabilities are estimated by the statistical analysis of the uncertainty or variability associated with the functions variables. In most cases, the analytical solution of the probability of failure is very difficult and sometimes almost practically impossible. However, by incorporating the Monte Carlo simulation method, this setback can be addressed. This method is normally used in structural reliability predictions and represents only half of a safety assessment (as it does not consider the severity of the failure) (Bangash (i983), Damkilde and Krenk (1997)). [Pg.50]

Performance-influencing factors analysis is an important part of the human reliability aspects of risk assessment. It can be applied in two areas. The first of these is the qualitative prediction of possible errors that could have a major impact on plant or personnel safety. The second is the evaluation of the operational conditions under which tasks are performed. These conditions will have a major impact in determining the probability that a particular error will be committed, and hence need to be systematically assessed as part of the quantification process. This application of PIFs will be described in Chapters 4 and 5. [Pg.105]

Chemicals risk assessment attempts to characterize the system — of a chemical in the environment or in the human body — and then estimate the risk of the chemical causing harm. A key question is whether the system can be adequately characterized to reliably estimate the probability of harm, or even to identify what harm the chemical may cause. Just as engineering systems have caused harm because they failed in ways that were not predicted, the most serious hazards of synthetic chemicals have often been unexpected, and not considered in risk assessments. [Pg.98]

Temperature indices are useful but limited in application as discussed earlier in this section. Until improved methods of assessment become available predictions based on chemical composition are likely to yield the most useful data. Tables 16.4 and 16.5 probably provide the most reliable indication of slagging propensity along with the base to acid ratio... [Pg.448]

This chapter deals with flood risk analysis and assessment. The conceptual model source pathway receptor consequence for flood risk analysis is presented and its components are analyzed. The methodology to extract the predicted probability of coastal flooding from risk sources and pathways, as well as the expected damages from risk receptors axe introduced and examined. Reliability analysis of a coastal system is also briefly discussed. Quantitative methods to define acceptable flooding probabilities on the level of the protected area are presented. Tools such as cost-benefit analysis, utihty models, and the life quality index are introduced to define the tolerable risk of flooding. [Pg.1039]

For any reliability assessment to be meaningful it is vital to address a specific system failure mode. Predicting the spurious shutdown frequency of a safety (shutdown) system will involve a different logic model and different failure rates from predicting the probability of failure to respond. ... [Pg.101]

A THERP tree is a technique used in human reliability assessment to calculate the probability of a human error during the execution of a task. (THERP stands for Technique for Human Error Rate Prediction.) A THERP tree is basically an event tree, where the root is the initiating event and the leaves are the possible outcomes. THERP is described in a publication from 1983 (Swain, A.D. and Guttmann, H.E., Handbook of Human Reliability Analysis with Emphasis on Nuclear Power Plant Applications, NUREG/CR-1278, USNRC), and is still widely used despite its unrealistic assumptions about human performance. One important... [Pg.88]

A limit-state is a condition beyond which a system no longer fulfills the desired functionality. Reliability assessment calculates and predicts the probability of limit-state violations at any stage of... [Pg.2269]

The Reactor Safety Study attempted to make a realistic estimate of the potential effects of LWR accidents on the public health and safety. One BWR, Peach Bottom Unit 2, and one PWR, Surry Unit 1, were analyzed in detail. The Reactor Safety Study team used previous information from Department of Defense and NASA to predict the effect of failures of small components in large, complex systems. Events that could potentially initiate core melt accidents were first identified. Event trees were then used to delineate possible sequences of successes or failures of systems provided to prevent core meltdown and/or the release of radionuclides. Fault trees were used to estimate the probabilities of system failures from available data on the reliability of system components. Using these techniques, thousands of possible core melt accident sequences were assessed for their occurrence probabilities. The consequences of such accident sequences were then estimated to complete the risk assessment. [Pg.52]

The starting point for all quantitative reliability assessments lies with the determination of values allocated to the primary events. The probability of a certain event occurring is usually derived from either predictive analysis, or relevant experience-based data, combined with assessment techniques such as fault tree analyses. This is illustrated in Fig. 10.4. [Pg.171]


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