A test station health monitoring system [military aircraft]

This paper presents a process to monitor test station health using the Weibull method and statistical patterns. The methodology is currently being applied to the F-16 automated test equipment (ATE) at the Ogden, Utah Air Logistic Center (OO-ALC) maintenance depot. An automated stream of test data collected from ATEs is used to process test results and to identify improvements necessary to increase the failure forecast accuracy. The paper discusses solutions to identify causes of 're-test OK' (RTOK) due to discrepancies between software testing procedures in the line and shop repairable units. The process includes a decision support system that uses artificial intelligence methods, such as expert system and neural networks, and a knowledge database to improve the troubleshooting capability. The paper also discusses a prototype development that collects malfunction codes (MFL) originated by the aircraft bus monitoring system. The MFL information is correlated with test results to detect RTOK causes.