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 (HUMS) have been developed in order to monitor the health condition of the gearbox, focusing towards early, accurate and on time fault detection with limited false alarms and missed detections. The main aim of a HUM System is by health monitoring to enhance the helicopters' operational reliability, to support the maintenance decision making, and to reduce the overall maintenance costs. The importance and the need for more advanced and accurate HUMS have been emphasized recently by the post-accident analysis of the helicopter LN-OJF, which crashed in Norway in 2016. During the last few decades various methodologies and diagnostic indicators/features have been proposed for the monitoring of rotating machinery operating under steady conditions but still there is no global solution for complex structures. A new tool called IESFOgram has been recently proposed by the authors, based on Cyclostationary Analysis, focusing on the accurate selection of a filtering band, under steady and varying speed conditions. Moreover the Cyclic Spectral Coherence is integrated along the selected frequency band leading to an Improved Envelope Spectrum. In this paper the performance of the tool is tested on a complex planetary gearbox, with several vibration sources. The method is tested, evaluated and compared to state of the art methods on a dataset captured during experimental tests under various operating conditions on a Category A Super Puma SA330 main planetary gearbox, presenting seeded bearing defects of different sizes.
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