Detection of faults in a gearbox is a first and foremost step before diagnostic and prognostic operations are performed. This paper proposes a novel gearbox fault detection and feature extraction technique. The proposed method adaptively filters the vibration signals emanating from a gearbox. A bandpass filter is designed and optimized through particle swarm optimization (PSO) to maximize kurtosis as an objective function. Gearbox health-related features are extracted from the filtered signals using second-order transient analysis. The method is validated on experimental data collected from a running gearbox in healthy and faulty conditions. The proposed method has successfully identified the faulty conditions inside the gearbox.

References

References
1.
Ciandrini
,
C.
,
Gallieri
,
M.
,
Giantomassi
,
A.
,
Ippoliti
,
G.
, and
Longhi
,
S.
,
2010
, “
Fault Detection and Prognosis Methods for a Monitoring System of Rotating Electrical Machines
,”
IEEE
International Symposium on Industrial Electronics
, July 4–7, pp.
2085
2090
.
2.
Lim
,
W. Q.
,
Zhang
,
D. H.
,
Zhou
,
J. H.
,
Belgi
,
P. H.
, and
Chan
,
H. L.
,
2010
, “
Vibration-Based Fault Diagnostic Platform for Rotary Machines
,”
IECON
2010–36th Annual Conference on IEEE Industrial Electronics Society
, Nov. 7–10, pp.
1404
1409
.
3.
Vieira
,
P.
,
Bobi
,
M. A. S.
,
Gomes
,
C. R.
,
Gomes
,
H. S.
, and
Nascimento
,
M. P. D.
,
2010
, “
Vibration Monitoring of Electric Generators Without Sensor Dedicated
,”
IEEE International Conference on Industrial Technology
(
ICIT
), pp.
451
456
.
4.
Zaidi
,
S. S. H.
,
Aviyente
,
S.
,
Salman
,
M.
,
Shin
,
K. K.
, and
Strangas
,
E. G.
,
2011
, “
Prognosis of Gear Failures in DC Starter Motors Using Hidden Markov Models
,”
IEEE Trans. Ind. Electron.
,
58
(
5
), pp.
1695
1706
.
5.
Lu
,
S.
,
He
,
Q.
,
Zhang
,
H.
, and
Kong
,
F.
,
2015
, “
Enhanced Rotating Machine Fault Diagnosis Based on Time-Delayed Feedback Stochastic Resonance
,”
ASME J. Vib. Acoust.
,
137
(
5
), p.
051008
.
6.
Wang
,
D.
,
Miao
,
Q.
,
Zhou
,
Q.
, and
Zhou
,
G.
,
2015
, “
An Intelligent Prognostic System for Gear Performance Degradation Assessment and Remaining Useful Life Estimation
,”
ASME J. Vib. Acoust.
,
137
(
2
), p.
021004
.
7.
Urbanek
,
J.
,
Barszcz
,
T.
, and
Antoni
,
J.
,
2013
, “
Time–Frequency Approach to Extraction of Selected Second-Order Cyclostationary Vibration Components for Varying Operational Conditions
,”
Measurement
,
46
(
4
), pp.
1454
1463
.
8.
Antoni
,
J.
,
2006
, “
The Spectral Kurtosis: A Useful Tool for Characterising Non-Stationary Signals
,”
Mech. Syst. Signal Process.
,
20
(
2
), pp.
282
307
.
9.
Antoni
,
J.
, and
Randall
,
R. B.
,
2006
, “
The Spectral Kurtosis: Application to the Vibratory Surveillance and Diagnostics of Rotating Machines
,”
Mech. Syst. Signal Process.
,
20
(
2
), pp.
308
331
.
10.
Ghods
,
A.
, and
Lee
,
H.-H.
,
2016
, “
Probabilistic Frequency-Domain Discrete Wavelet Transform for Better Detection of Bearing Faults in Induction Motors
,”
Neurocomputing
,
188
, pp.
206
216
.
11.
Immovilli
,
F.
,
Cocconcelli
,
M.
,
Bellini
,
A.
, and
Rubini
,
R.
,
2009
, “
Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals
,”
IEEE Trans. Ind. Electron.
,
56
(
11
), pp.
4710
4717
.
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
.
13.
Assaad
,
B.
,
Eltabach
,
M.
, and
Antoni
,
J.
,
2014
, “
Vibration Based Condition Monitoring of a Multistage Epicyclic Gearbox in Lifting Cranes
,”
Mech. Syst. Signal Process.
,
42
(
1–2
), pp.
351
367
.
14.
Al-Bugharbee
,
H.
, and
Trendafilova
,
I.
,
2016
, “
A Fault Diagnosis Methodology for Rolling Element Bearings Based on Advanced Signal Pretreatment and Autoregressive Modelling
,”
J. Sound Vib.
,
369
, pp.
246
265
.
15.
Antoni
,
J.
,
Bonnardot
,
F.
,
Raad
,
A.
, and
El Badaoui
,
M.
,
2004
, “
Cyclostationary Modelling of Rotating Machine Vibration Signals
,”
Mech. Syst. Signal Process.
,
18
(
6
), pp.
1285
1314
.
16.
Boungou
,
D.
,
Guillet
,
F.
,
Badaoui
,
M. E.
,
Lyonnet
,
P.
, and
Rosario
,
T.
,
2015
, “
Fatigue Damage Detection Using Cyclostationarity
,”
Mech. Syst. Signal Process.
,
58–59
, pp.
128
142
.
17.
Lamraoui
,
M.
,
Thomas
,
M.
, and
El Badaoui
,
M.
,
2014
, “
Cyclostationarity Approach for Monitoring Chatter and Tool Wear in High Speed Milling
,”
Mech. Syst. Signal Process.
,
44
(
1–2
), pp.
177
198
.
18.
Antoni
,
J.
, and
Randall
,
R. B.
,
2002
, “
Differential Diagnosis of Gear and Bearing Faults
,”
ASME J. Vib. Acoust.
,
124
(
2
), pp.
165
171
.
19.
Antoni
,
J.
, and
Randall
,
R. B.
,
2003
, “
A Stochastic Model for Simulation and Diagnostics of Rolling Element Bearings With Localized Faults
,”
ASME J. Vib. Acoust.
,
125
(
3
), pp.
282
289
.
20.
Antoni
,
J.
,
2007
, “
Fast Computation of the Kurtogram for the Detection of Transient Faults
,”
Mech. Syst. Signal Process.
,
21
(
1
), pp.
108
124
.
21.
Antoni
,
J.
,
2016
, “
The Infogram: Entropic Evidence of the Signature of Repetitive Transients
,”
Mech. Syst. Signal Process.
,
74
, pp.
73
94
.
22.
Hussain
,
S.
, and
Gabbar
,
H. A.
,
2011
, “
A Novel Method for Real Time Gear Fault Detection Based on Pulse Shape Analysis
,”
Mech. Syst. Signal Process.
,
25
(
4
), pp.
1287
1298
.
23.
Hussain
,
S.
, and
Gabbar
,
H. A.
,
2013
, “
Fault Diagnosis in Gearbox Using Adaptive Wavelet Filtering and Shock Response Spectrum Features Extraction
,”
Struct. Health Monit.
,
12
(
2
), pp.
169
180
.
24.
Soleimani
,
A.
, and
Khadem
,
S. E.
,
2015
, “
Early Fault Detection of Rotating Machinery Through Chaotic Vibration Feature Extraction of Experimental Data Sets
,”
Chaos, Solitons Fractals
,
78
, pp.
61
75
.
25.
He
,
G.
,
Ding
,
K.
, and
Lin
,
H.
,
2016
, “
Fault Feature Extraction of Rolling Element Bearings Using Sparse Representation
,”
J. Sound Vib.
,
366
, pp.
514
527
.
26.
Wang
,
H.
,
Chen
,
J.
, and
Dong
,
G.
,
2014
, “
Feature Extraction of Rolling Bearing's Early Weak Fault Based on EEMD and Tunable Q-Factor Wavelet Transform
,”
Mech. Syst. Signal Process.
,
48
(
1–2
), pp.
103
119
.
27.
Wang
,
S.
,
Cai
,
G.
,
Zhu
,
Z.
,
Huang
,
W.
, and
Zhang
,
X.
,
2015
, “
Transient Signal Analysis Based on Levenberg–Marquardt Method for Fault Feature Extraction of Rotating Machines
,”
Mech. Syst. Signal Process.
,
54–55
, pp.
16
40
.
28.
Iba
,
H.
,
Hasegawa
,
Y.
, and
Paul
,
T. K.
,
2009
,
Applied Genetic Programming and Machine Learning
,
Taylor and Francis Group
, Boca Raton, FL.
29.
Kennedy
,
J.
, and
Eberhart
,
R.
,
1995
, “
Particle Swarm Optimization
,”
IEEE International Conference on Neural Networks
, Vol.
1944
, pp.
1942
1948
.
30.
Samanta
,
B.
, and
Nataraj
,
C.
,
2009
, “
Use of Particle Swarm Optimization for Machinery Fault Detection
,”
Eng. Appl. Artif. Intell.
,
22
(
2
), pp.
308
316
.
31.
Chen
,
D.
, and
Zhao
,
C.
,
2009
, “
Particle Swarm Optimization With Adaptive Population Size and Its Application
,”
Appl. Soft Comput.
,
9
(
1
), pp.
39
48
.
32.
Chen
,
F.
,
Tang
,
B.
,
Song
,
T.
, and
Li
,
L.
,
2014
, “
Multi-Fault Diagnosis Study on Roller Bearing Based on Multi-Kernel Support Vector Machine With Chaotic Particle Swarm Optimization
,”
Measurement
,
47
, pp.
576
590
.
33.
Pan
,
H.
,
Wei
,
X.
, and
Xu
,
X.
,
2010
, “
Research of Optimal Placement of Gearbox Sensor Based on Particle Swarm Optimization
,” 8th
IEEE
International Conference on Industrial Informatics
, July 13–16, pp.
108
113
.
34.
Hongxia
,
P.
,
Qingfeng
,
M.
, and
Xiuye
,
W.
,
2006
, “
Research on Fault Diagnosis of Gearbox Based on Particle Swarm Optimization Algorithm
,” 8th
IEEE
International Conference on Mechatronics
, July 3–5, pp.
32
37
.
35.
Zhu
,
Z. K.
,
Yan
,
R.
,
Luo
,
L.
,
Feng
,
Z. H.
, and
Kong
,
F. R.
,
2009
, “
Detection of Signal Transients Based on Wavelet and Statistics for Machine Fault Diagnosis
,”
Mech. Syst. Signal Process.
,
23
(
4
), pp.
1076
1097
.
36.
da Silva
,
S. P.
,
Filho
,
S. L. M. R.
, and
Brandão
,
L. C.
,
2014
, “
Particle Swarm Optimization for Achieving the Minimum Profile Error in Honing Process
,”
Precis. Eng.
,
38
(
4
), pp.
759
768
.
37.
Meriam
,
J. L.
, and
Kraige
,
L. G.
,
2013
,
Engineering Mechanics: Dynamics
,
Wiley
, New York.
38.
Hassan
,
R.
,
Cohanim
,
B.
,
De Weck
,
O.
, and
Venter
,
G.
, 2005, “
A Comparison of Particle Swarm Optimization and the Genetic Algorithm
,”
AIAA
Paper No. 2015-1897.
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