Abstract

The core of a helicopter drivetrain is a complex planetary main gearbox (MGB) which reduces the high input speed generated by the engines in order to provide the appropriate torque to the main rotors and to other auxiliary systems. The gearbox consists of various shafts, planetary gears and bearings and operates under varying conditions under excessive friction, heat and high mechanical forces. The components are vulnerable to fatigue defects and therefore Health and Usage Monitoring Systems have been developed to monitor the gearbox health condition, focusing towards early and accurate fault detection with limited false alarms and missed detections. HUMS's aim is to enhance the helicopters' operational reliability, supporting the maintenance decision making, and reducing the overall maintenance costs. The need for more advanced HUMS have been emphasized by the post-accident analysis of the helicopter LN-OJF, which crashed in Norway in 2016. During the last decades various methodologies have been proposed for monitoring of rotating machinery operating under steady conditions, without having still a global solution. A new tool (IESFOgram) has been recently proposed by the authors focusing on the selection of a filtering band, under steady and varying speed conditions. The Cyclic Spectral Coherence is integrated along the selected frequency band leading to an Improved Envelope Spectrum. In this paper the performance of the method is evaluated and compared to state of the art methods on a dataset captured on a Category A Super Puma SA330 main planetary gearbox, presenting seeded bearing defects of different sizes.

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