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 (HUMS) have been developed in order to monitor the health condition of the gearbox, focusing toward early, accurate, and on-time fault detection with limited false alarms and missed detections. The main aim of a HUMS 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 postaccident 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 improved envelope spectrum via feature optimization-gram (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 (CSCoh) is integrated along the selected frequency band leading to an improved envelope spectrum (IES). 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.

References

1.
Zhou
,
L.
,
Duan
,
F.
,
Corsar
,
M.
,
Elasha
,
F.
, and
Mba
,
D.
,
2017
, “
A Study on Helicopter Main Gearbox Planetary Bearing Fault Diagnosis
,”
Appl. Acoust.
, 147, pp.
4
14
.10.1016/j.apacoust.2017.12.004
2.
Elasha
,
F.
,
Greaves
,
M.
, and
Mba
,
D.
,
2017
, “
Bearing Signal Separation of Commercial Helicopter Main Gearbox
,”
Procedia CIRP
,
59
, pp.
111
115
.10.1016/j.procir.2016.09.030
3.
Antoni
,
J.
,
Griffaton
,
J.
,
André
,
H.
,
Avendaño-Valencia
,
L. D.
,
Bonnardot
,
F.
,
Cardona-Morales
,
O.
,
Castellanos-Dominguez
,
G.
,
Daga
,
A. P.
,
Leclère
,
Q.
,
Vicuña
,
C. M.
,
Acuña
,
D. Q.
,
Ompusunggu
,
A. P.
, and
Sierra-Alonso
,
E. F.
,
2017
, “
Feedback on the Surveillance 8 Challenge: Vibration-Based Diagnosis of a Safran Aircraft Engine
,”
Mech. Syst. Signal Process.
,
97
, pp.
112
144
.10.1016/j.ymssp.2017.01.037
4.
Mauricio
,
A.
,
Qi
,
J.
, and
Gryllias
,
K.
,
2018
, “
Vibration Based Condition Monitoring of Wind Turbine Gearboxes Based on Cyclostationary Analysis
,”
ASME
Paper No. GT2018-76993.10.1115/GT2018-76993
5.
Randall
,
R.
, and
Sawalhi
,
N.
,
2011
, “
Use of the Cepstrum to Remove Selected Discrete Frequency Components From a Time Signal
,” Rotating Machinery, Structural Health Monitoring, Shock and Vibration, Vol.
5
, Conference Proceedings of the Society for Experimental Mechanics Series, Springer, New York, pp.
451
461
.
6.
McFadden
,
P.
, and
Smith
,
J.
,
1984
, “
Vibration Monitoring of Rolling Element Bearings by the High-Frequency Resonance Technique—A Review
,”
Tribol. Int.
,
17
(
1
), pp.
3
10
.10.1016/0301-679X(84)90076-8
7.
Antoni
,
J.
,
2007
, “
Fast Computation of the Kurtogram for the Detection of Transient Faults
,”
Mech. Syst. Signal Process.
,
21
(
1
), pp.
108
124
.10.1016/j.ymssp.2005.12.002
8.
Gryllias
,
K.
, and
Antoniadis
,
I.
,
2009
, “
A Peak Energy Criterion (PE) for the Selection of Resonance Bands in Complex Shifted Morlet Wavelet (CSMW) Based Demodulation of Defective Rolling Element Bearings Vibration Response
,”
Int. J. Wavelets, Multiresolution Inf. Process.
,
7
(
4
), pp.
387
410
.10.1142/S0219691309002982
9.
Smith
,
W.
,
Randall
,
R.
,
du Mée
,
X. C.
, and
Peng
,
P.
,
2017
, “
Use of Cyclostationary Properties to Diagnose Planet Bearing Faults in Variable Speed Conditions
,”
Tenth DST Group International Conference on Health and Usage Monitoring Systems
, Melbourne, Australia, Feb. 26–28, pp.
1
7
.https://www.researchgate.net/publication/316284567_Use_of_Cyclostationary_Properties_to_Diagnose_Planet_Bearing_Faults_in_Variable_Speed_Conditions
10.
Antoni
,
J.
,
2007
, “
Cyclic Spectral Analysis in Practice
,”
Mech. Syst. Signal Process.
,
21
(
2
), pp.
597
630
.10.1016/j.ymssp.2006.08.007
11.
Antoni
,
J.
,
Abboud
,
D.
, and
Xin
,
G.
,
2016
, “
Cyclostationarity in Condition Monitoring: 10 Years After
,”
ISMA 2016 Including USD 2016
, Leuven, Belgium, Sept. 19–21, pp.
2365
2376
.http://past.isma-isaac.be/downloads/isma2016/papers/isma2016_0246.pdf
12.
Abboud
,
D.
,
Baudin
,
S.
,
Antoni
,
J.
,
Rémond
,
D.
,
Eltabach
,
M.
, and
Sauvage
,
O.
,
2016
, “
The Spectral Analysis of Cyclo-Non-Stationary Signals
,”
Mech. Syst. Signal Process.
,
75
, pp.
280
300
.10.1016/j.ymssp.2015.09.034
13.
Antoni
,
J.
,
Xin
,
G.
, and
Hamzaoui
,
N.
,
2017
, “
Fast Computation of the Spectral Correlation
,”
Mech. Syst. Signal Process.
,
92
, pp.
248
277
.10.1016/j.ymssp.2017.01.011
14.
Randall
,
R.
,
Antoni
,
J.
, and
Chobsaard
,
J.
,
2001
, “
The Relationship Between Spectral Correlation and Envelope Analysis in the Diagnostics of Bearing Faults and Other Cyclostationary Machine Signals
,”
Mech. Syst. Signal Process.
,
15
(
5
), pp.
945
962
.10.1006/mssp.2001.1415
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