The Team of America's Phenix, Bayesian Systems and Gryphon Technologies proposes applying Bayes'Engine (TM) as an innovative non-destructive inspection (NDI) technique for increasing the number of successfully reworked gas turbine engine nozzle guide vane (NGV) segments. The proposed applications is based on the premise of consolidating available post-assembly (rework) data for the NGV segments on the Rolls Royce F402 and General Electric F404 engines that are reworked at the Naval Engine Airfoil Center (Cherry Point, NC). Bayes Theorem is the fundamental mathematical/logical principle governing the process of using logical inference or reasoning from available data to drawing a conclusion. Applying Bayes Theorem to the NGV segment rework process will account for all available relevant information (such as engine history, usage and maintenance data). Using this information and applying the Bayesian logic, the tool will develop probability curves for the possible outcomes at each available decision option. These curves, along with cost information, are input to a Decision Theory module which quantifies the associated risk in continuing the rework process (whether at the inception point or the end of the process) and recommends the optimal decision on whether to rework, continue rework or scrap the NGV segment.
Keywords: Bayes Engine / Theorem Probability Quantified Risk Assessments Nozzle Guide Vane Segments Maintenanc
