Article ID: | iaor20131315 |
Volume: | 112 |
Issue: | 2 |
Start Page Number: | 38 |
End Page Number: | 47 |
Publication Date: | Apr 2013 |
Journal: | Reliability Engineering and System Safety |
Authors: | Pan Rong, Wang Lizhi, Li Xiaoyang, Jiang Tongmin |
Keywords: | Bayesian analysis, Markov chain Monte Carlo |
Accelerated degradation testing (ADT) is a common approach in reliability prediction, especially for products with high reliability. However, oftentimes the laboratory condition of ADT is different from the field condition; thus, to predict field failure, one need to calibrate the prediction made by using ADT data. In this paper a Bayesian evaluation method is proposed to integrate the ADT data from laboratory with the failure data from field. Calibration factors are introduced to calibrate the difference between the lab and the field conditions so as to predict a product's actual field reliability more accurately. The information fusion and statistical inference procedure are carried out through a Bayesian approach and Markov chain Monte Carlo methods. The proposed method is demonstrated by two examples and the sensitivity analysis to prior distribution assumption.