The US Air Force (USAF)’s goal is to achieve aircraft availability (AA) rates similar to those of the commercial airline industry. Improving AA rates while reducing operations and maintenance (O&M;) costs is a major challenge for the USAF. This Praxis research identifies field-level components for inclusion into periodic scheduled planned preventive maintenance (PM) based on a reliability component-life-failure prediction analysis.This praxis analyzes the USAF reliability centered maintenance (RCM) maintenance strategy on a fighter aircraft landing gear system and its components that are allowed to run-to-failure (RTF), fix when failed. This praxis uses historical empirical life-prediction failure data, selecting the Weibull – 2 parameter probability distribution model to perform the reliability analysis on the data. The data sets determines the selection of the distribution model through the maximum likelihood estimation method (MLE), are examined. The praxis uses Monte Carlo simulations to verify the Weibull – 2 parameter reliability analysis results and theoretically configures the landing gear system serially to perform the system reliability analysis. The system reliability analysis was performed using a reliability block diagram (RBD) to establish a worst-case scenario for the landing gear system reliability measures. This praxis final methodological step uses the components’ reliability analysis results to perform optimal preventive replacement age model (OPRAM) simulations. The OPRAM simulations produce the availability and cost value functions of both PM tasks and unplanned corrective maintenance (CM) activities. This reliability analysis methodology of using the OPRAM simulations proves that including periodic scheduled PM tasks improves AA rates and reduces O&M; costs. This praxis recommends applying this reliability analysis methodology to the USAF-designated RTF components be placed in the periodic schedule preventive maintenance task category. This praxis recommends that this reliability analysis methodology approach, with OPRAM simulations, be incorporated into future maintenance strategy decisions.
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