Bayesian reliability prediction of a medical device system

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 uncertainty quantified as confidence intervals. This is achieved by propagating the uncertainty of model parameters and component-level reliability to the system level. In this work, Bayesian models are applied in the Design for Reliability process to assess medical device system reliability with confidence. In cases where a single data source is insufficient due to sample size limitations, Bayesian models are flexible to aggregate various sources of data to reduce uncertainty.