05/00903 Principles of failure probability assessment (PoF)

2005 ◽  
Vol 46 (2) ◽  
pp. 130
Keyword(s):  
Failure Probability ◽  
Probability Assessment

Failure Probability Assessment of Piping Systems Affected by SCC and Pipe Wall Thinning for LBB Analysis

2011 ◽  
Author(s):  
Hiromasa Chitose ◽  
Hideo Machida ◽  
Itaru Saito
Keyword(s):  
Failure Probability ◽  
Nuclear Power ◽  
Power Plants ◽  
Pipe Wall ◽  
Probability Assessment ◽  
Wall Thinning ◽  
Piping Systems ◽  
Leak Before Break ◽  
Thinning Rate ◽  
Service Inspection

This paper provides failure probability assessment results for piping systems affected by stress corrosion cracking (SCC) and pipe wall thinning in nuclear power plants. On the basis of the results, considerations for applying the leak-before-break (LBB) concept in actual plants are presented. The failure probability for SCC satisfies the target failure probability even if conservative conditions are assumed. Moreover, for pipe wall thinning analysis, pre-service inspection is important for satisfying the target failure probability because the initial wall thickness affects the accuracy of the wall thinning rate. The pipe wall thinning analysis revealed that the failure probability is higher than the target probability if the bending stress in the pipe is large.

scholarly journals Bayesian assessment of the variability of reliability measures

2006 ◽  
Vol 26 (1) ◽  
pp. 109-127 ◽  
Author(s):  
Enrique López Droguett ◽  
Frank J. Groen ◽  
Ali Mosleh
Keyword(s):  
Failure Rate ◽  
Failure Probability ◽  
Bayesian Updating ◽  
Estimation Procedure ◽  
Population Variability ◽  
Probability Assessment ◽  
Time Data ◽  
Reliability Measures ◽  
Variability Analysis ◽  
Variability Model

Population variability analysis, also known as the first stage in two-stage Bayesian updating, is an estimation procedure for the assessment of the variability of reliability measures among a group of sub-populations of similar systems. The estimated variability distributions are used as prior distributions in system-specific Bayesian updates. In this paper we present a Bayesian approach for population variability analysis involving the use of non-conjugate variability models that works over a continuous, rather than the discretized, variability model parameter space. The cases to be discussed are the ones typically encountered by the reliability practitioner: run-time data for failure rate assessment, demand-based data for failure probability assessment, and expert-based evidence for failure rate and failure probability analysis. We outline the estimation procedure itself as well as its link with conventional Bayesian updating procedures, describe the results generated by the procedures and their behavior under various data conditions, and provide numerical examples.

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