Application of lubricants in the ah - 64 d helicopter gearboxes for improvement of condition - based maintenance Practices

This dissertation aims to develop new condition-based maintenance (CBM) tools for increasing performance of Apache helicopter drivetrain gearboxes, through lubrication and signal analysis. An extensive consumer of CBM is the Apache helicopter that involves testing critical components to demonstrate reliability and performance, measured through data-driven condition indicators (CIs). These indicators provide vital information about the condition of a mechanical component and are derived from onboard sensors using signal processing methods. A challenge known among researchers in the Apache community is leakage of grease lubricant from drivetrain gearboxes with performance of some CIs unachieved. Although extensive maintenance operations are used in the field to minimize this effect by ground inspections, failure of grease lubricant still occurs due to heavy loads. Based on this motivation, oil nanofluids are presented here as a new lubricant approach for the intermediate gearbox (IGB) of the Apache helicopter. Furthermore, a signal-based approach utilizing wavelet analysis is adapted to develop a new CI. The goal of this work can be achieved through addressing the following research studies. The first study qualifies the improved thermophysical properties of two turbine jet oil nanofluid samples and eight Mobil Aviation Gear Lubricant (AGL) oil nanofluid samples with different particle concentrations and chemical compositions. Numerous fluid properties such as thermal conductivity, dynamic viscosity, and viscosity index are measured using off-line experimental tools. Based on the experimental results, four AGL oils with graphite nanoparticles have displayed excellent thermophysical properties and are used as prospective lubricants for mechanical testing in the IGB.