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- Title
BAYESIAN INFERENCE TO ESTIMATE RANDOM FAILURE PROBABILITY.
- Authors
Hyun Su Sim; Yong Soo Kim
- Abstract
This study introduces a process based on Bayesian inference, enhancing the accuracy of random failure probability estimation, outlined in a detailed six-step procedure. This method focuses on comprehensive data analysis and precise probability estimations, proving particularly beneficial for limited datasets. Applied to brake disc random failure probability assessment, our approach's results were compared with those obtained through Maximum Likelihood Estimation (MLE) across various specimen sizes. This comparative analysis included both graphical and statistical evaluations. The experimental findings demonstrate that our Bayesian inference-based process effectively addresses the challenges posed by small datasets, significantly enhancing estimation accuracy. This methodology is especially advantageous in scenarios where data collection is difficult, providing reliability engineers with an essential framework for leveraging prior information to improve risk management in diverse industrial applications.
- Subjects
BAYESIAN field theory; MAXIMUM likelihood statistics; RELIABILITY in engineering; DISC brakes; ACQUISITION of data
- Publication
International Journal of Industrial Engineering, 2023, Vol 30, Issue 6, p1525
- ISSN
1072-4761
- Publication type
Article
- DOI
10.23055/ijietap.2023.30.6.9595