In fatigue life design against the crack failure of critical assets such as the gas turbine blades, common practice is to apply safety factor to account for associated uncertainties. However, the approach tends to disagree with field observations, which mandates the scheduled maintenance. Furthermore, the inspection period established by the design stage, tends to be too conservative, impacting the cost and safety.  To overcome this, Bayesian approach is applied, which exploits analytic prediction as the prior and field inspection data as the likelihood to determine the lower confidence bound of B1 life. The results provide valuable information for more reliable and affordable inspection scheduling.