A Comparative Study of Helicopter Planetary Bearing
Diagnosis with Vibration and Acoustic Emission Data
Linghao Zhou, Fang Duan, David Mba
School of Engineering
London South Bank University
London, U. K.
Zhoul7@lsbu.ac.uk
Elasha Faris
School of Mechanical, Automotive and Aerospace
Coventry University
Coventry, U.K.
ac1027@coventry.ac.uk
Abstract— Planetary bearings inside a helicopter main gearbox
(MGB) are key components, successful diagnosis of planetary
bearing defects benefits helicopter maintenance, reducing
accidents rate and increasing aircraft flightworthiness. Most
widely adopted methods for bearing fault diagnosis are envelope
analysis and kurtogram; both have achieved many successes in
practical application. However, diagnosis of faulty planetary
bearing inside a MGB can be more complicated for reasons such
as extremely strong operating noise, overwhelming gear meshes
and so on. In this paper, acoustic emission (AE) data was
recorded in comparison with traditional vibration data from test
rig built using commercial helicopter gearbox with seeded
defects, diagnosis of vibration data and AE data is performed
using kurtogram and envelope analysis, results showed that AE
signals are more sensitive to excitations from defects and suffer
less from background noises.
Keywords- helicopter main gearbox; planetary bearing
diangosis; acoustic emission signal; kurtogram; envelope analysis
I. I
NTRODUCTION
Bearings are one of the most crucial components in a
helicopter main gearbox (MGB). MGB reduces high input
speed generated from engines, hence providing low output
speed with high torque to drive the main rotors [1]. Usually the
speed reduction rate is so large that strong stress and forces are
applied to planetary bearings, making them prone to all kinds
of defects including bearing pitting, spalling and contact wear
[2]. Helicopter health and usage monitoring system (HUMS)
was first developed during 1990s and installed on medium and
large civil helicopters to monitor aircraft flight status [3].
HUMS collects helicopters vibration data during specific flight
regimes, and processes the data at Signal Processing Unit
(SPU), generating condition indicators (CIs). Most CIs have
pre-set thresholds which trigger the system alarms once being
breached. More detailed data analysis however, is performed at
ground base station, with data stored at a storage unit [4].
Installation of HUMS has evidently reduced accidents rate,
according to a survey carried out by US Joint Helicopter Safety
Implementation Team [4]. But as a continuously evolving
system, HUMS is still not completely accurate and reliable for
incipient fault detection. Some tragic helicopter crashes
happened in recent decade indicate that HUMS failed to
diagnose incipient defects happened on planetary bearings
inside MGB before flight. During flight small cracks
propagated and developed across MGB case, thus eventually
led to rotor imbalance and aircraft disassembly [5-6].
Multiple reasons could contribute to HUMS not being able
to react properly to incipient planetary bearing defects,
including but not limited to:
• Epicyclic Modules of MGB can achieve a large
reduction rate of approximately 86:1 (calculated based
on test rig MGB). Such functionality is realised by a
sophisticated mechanical structure, which involves
many planetary gears and bearings (see Fig. 1 and Fig.
2). This structure complicates signal transmission paths
for accelerometers, hence vibration data collected are
highly amplitude and frequency modulated, which
potentially masks faulty signature.
• Decided by the structure of planetary gear/ bearing set,
gears and bearings share same races, which result in
overwhelming gear meshes signal masking planetary
bearing signals that are inherently weak. Thus faulty
signatures excited by contacts between defects and
rollers/ cages are difficult to be extracted.
• An operating helicopter MGB, especially in high-speed
mode, generates extremely large noise from the
frictions and contacts between MGB components.
Signal-to-noise (SNR) ratio is not idea enough for
direct clear diagnosis under such harsh circumstances.
• Traditional HUMS CIs are generated based on
statistical characteristics of vibration data, and
sometime interpreting statistical features can be tricky.
For example, kurtosis can be used to describe shape of
the probability distribution of the vibration data, i.e.
how flat or steep the data are. However kurtosis will
lose its validity once the initial defects propagate into
more distributed or severed defects. In addition, low
SNR affects CIs’ accuracy drastically.
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