Abstract
Helicopters are extensively used in civil applications as they are versatile in their capabilities to manoeuvre. Their operation under harsh conditions and environments demand for a strict maintenance plan. Main gearboxes (MGB) of helicopters are a critical component responsible for reducing the high input speed generated from the gas turbine engines. Health and Usage Monitoring Systems (HUMS) are installed in an effort to monitor the health state of the transmission systems, and ideally, to detect and diagnose the type of a generated fault. Even though the development of HUMS contributed to the reduction of worldwide helicopter accident rate, more advanced systems are needed based on the investigation of the air accidents of AS332 L2 Super Puma in Scotland in 2009 and of EC225 LP Super Puma in Bergen in 2016, due to failure of a planet gear of the MGB. A plethora of signal processing methodologies have been proposed for the early detection of faults but often they fail in complex structures, such as planetary gearboxes operating under various conditions. In this paper the performance of a recently proposed diagnostic tool, called IESFOgram, is evaluated and compared with state of the art techniques, applied on signal captured on a Category A Super Puma SA330 MGB.
Highlights
Health and Usage Monitoring Systems (HUMS) conduct helicopter health monitoring by extracting Health Indicators (HI) from acquired vibration data
The key objective of this paper is to evaluate the performance of the IESFOgram and compare it with state of the art methods by applying it on real vibration data acquired from a Main gearboxes (MGB) of a Super Puma under various levels of damage on the planet bearing, operating under different speed and load conditions
The Improved Envelope Spectrum (IES) based on the IESFOgram is proposed for diagnosis of bearing diagnostics of helicopter’s main gearbox
Summary
Health and Usage Monitoring Systems (HUMS) conduct helicopter health monitoring by extracting Health Indicators (HI) from acquired vibration data. One of the most common approaches in bearing fault detection is the Envelope Analysis, where the signal is demodulated in order to detect the lower cyclic modulations of the bearing. This method is sensitive to the noise, so it is commonly used alongside with a band pass filter around the band of frequencies excited by the impulses of the bearing fault. In the frames of the analysis of vibration data of a MGB with damaged bearing, Zhou et al [1] concluded that the Kurtogram was ineffective by itself, and the damage was only diagnosable after application of a self-adaptive noise cancellation filter
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