Bayesian methodology for system reliability assessment

Abstract

High product quality and reliability are critical in the medical device industry. Accurate reliability prediction during the product development stage provides inputs for the design strategy and boosts understanding and confidence in product reliability before products are released to the market. A Bayesian framework provides a straightforward solution for product system reliability prediction with quantified uncertainty. This is achieved by propogating the uncertainty of model parameters, use conditions, and component-level reliability to the system level. In cases where a single data source is insufficient to accurately estimate reliability due to sample size limitations or potential biases, Bayesian methods are flexible to aggregate various sources of data to reduce uncertainty. In this work, Bayesian models are applied in the Design for Reliability process to assess medical device system reliability with confidence, where frequentist methods may be approximate or non-existent.