Component reliability assessment using quantitative and qualitative data

Abstract The estimation of a component failure rate depends on the availability of plant specific numerical data. The purpose of this study was development of a new method that explicitly includes numerical and linguistic information into the assessment of a specific failure rate. The basis of the method is the Bayesian updating approach. A prior distribution is selected from a generic database, whereas likelihood is assessed using the principles of fuzzy set theory. The influence of component operating conditions on component failure rate is modeled using a fuzzy inference system. Results of fuzzy reasoning are then used for building an appropriate likelihood function for the Bayesian inference. The method was applied on a high voltage transformer. Results show that with the proposed method, one can estimate the specific failure rate and analyze possible measures to improve component reliability. The method can be used for specific applications including components for which there is not enough numerical data for specific evaluation.

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