Perspectives on Health and Usage Monitoring
Systems (HUMS) of helicopters
Alexandre Mauricio
1,2
, Junyu Qi
1,2
, Linghao Zhou
3
, Wenyi Wang
4
, David Mba
5
,
and Konstantinos Gryllias
1,2,*
1
Department of Mechanical Engineering, KU Leuven, 3001, Leuven, Belgium
2
Dynamics of Mechanical and Mechatronic Systems, Flanders Make, 3001, Leuven, Belgium
3
London South Bank University, School of Engineering, SE1 0AA, London, UK
4
Defence Science and Technology Organization (DSTO), Air Vehicles Division, Melbourne, VIC
3011, Australia
5
De Montfort University, Faculty of Technology, LE1 9BH, Leicester, UK
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.
1 Introduction
Health and Usage Monitoring Systems (HUMS) conduct helicopter health monitoring by
extracting Health Indicators (HI) from acquired vibration data. However, HI’s have a wide
range of values depending on a plethora of conditions of the helicopter besides their health
state (e.g. temperature, speed, torque, etc.), making the implementation of specific thresholds
of the HI often unreliable for the health monitoring. Analysis of the time-domain of the
vibration data is also non-practical, as MGB of helicopters are complex machinery with many
sources. Analysis of the frequency-domain provides a more direct way to monitor the health
condition of its components by tracking the amplitude at specific frequencies related to
specific components. Signal processing techniques are usually applied in this scenario in
*
Corresponding author: konstantinos.gyllias@kuleuven.be
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons
Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
MATEC Web of Conferences 314, 02008 (2020) https://doi.org/10.1051/matecconf/202031402008
ICSC-ISATECH 2019
