Acoustic emission (AE) signals are recognized as complementary measures for detecting incipient faults and condition monitoring in rotary machinery due to their containment of sources of potential fault energy. However, determining the potential sources of faults cannot be easily realized due to the non-stationarity of AE signals. Available techniques that are capable of evoking instantaneous characteristics of a particular AE signal cannot optimally perform in a sense that there is no guarantee that these characteristics (hereinafter referred to as the “features”) remain constant when another AE signal is obtained from the system, albeit operating under the same machine condition at a different time instant. This paper provides a theoretical framework for developing a highly reliable classification and detection methodology for gas turbine condition monitoring based on AE signals. Mathematical results obtained in this paper are evaluated and validated by using actual gas turbines that are operating in power generating plants, to demonstrate the practicality and simplicity of our methodologies. Emphasis is given to acoustic emissions of similar brand and sized gas turbine turbomachinery under different health conditions and/or aging characteristics.

References

References
1.
International Standards Organization (ISO)
,
2007
, DIS Standard 22096, Condition Monitoring and Diagnosis of Machines Acoustic Emission.
2.
Douglas
,
R. M.
,
Beugn
,
S.
,
Jenkins
,
M. D.
,
Frances
,
A. K.
,
Steel
,
J. A.
,
Reuben
,
R. L.
, and
Kew
,
P. A.
,
2004
,
Monitoring of Gas Turbine Operating Parameters Using Acoustic Emission
,
School of Engineering and Physical Sciences, Heriot-Watt University
,
Edinburgh, UK., Bridge of Don, Aberdeen, UK
.
3.
Nashed
,
M. S.
,
Steel
,
J. A.
, and
Reuben
,
R. L.
,
2014
, “
The Use of Acoustic Emission for the Condition Assessment of Gas Turbines: Acoustic Emission Generation From Normal Running
,”
J. Process Mech. Eng.
,
228
(
4
), pp.
286
308
.
4.
Farhat
,
S. A.
, and
Al-Taleb
,
M. K.
,
2010
, “
Combustion Oscillations Diagnostics in a Gas Turbine Using An Acoustic Emissions
,”
Jordan J. Mech. Ind. Eng.
,
4
(
3
), pp.
352
357
.
5.
Armor
,
A. F.
,
Graham
,
L. J.
, and
Frank
,
R. L.
,
1981
, “
Acoustic Emission Monitoring of Steam Turbines
,”
Joint ASME/IEEE Power Generation Conference
,
St. Louis
, MO,
Oct. 4–8
.
6.
Sato
,
I.
,
1990
, “
Rotating Machinery Diagnosis With Acoustic Emission Techniques
,”
Electr. Eng. Jpn.
,
110
(
2
), pp.
115
127
.
7.
Mba
,
D.
, and
Hall
,
L. D.
,
2002
, “
The Transmission of Acoustic Emission Across Large-Scale Turbine Rotors
,”
NDT E Int.
,
35
(
8
), pp.
529
539
.
8.
Zuluaga-Giraldo
,
C.
,
Mba
,
D.
, and
Smart
,
M.
,
2004
, “
Acoustic Emission During Run-Up and Run-Down of a Power Generation Turbine
,”
Tribol. Int.
,
37
(
5
), pp.
415
422
.
9.
Purarjomandlangrudi
,
A.
, and
Nourbakhsh
,
G.
,
2013
,
Acoustic Emission Condition Monitoring: An Application For Wind Turbine Fault Detection
,
Queensland University of Technology (QUT)
,
George St Brisbane, QLD 4000
.
10.
Kuanfang
,
H.
,
Jigang
,
W.
, and
Guangbin
,
W.
,
2011
, “
Time-Frequency Entropy Analysis of Alternating Current Square Wave Current Signal in Submerged Arc Welding
,”
J. Comput.
,
6
(
10
), pp.
2092
2097
.
11.
Caesarendra
,
W.
,
Kosasih
,
B.
,
Tieu
,
A. K.
,
Zhu
,
H.
,
Moodie
,
C. A. S.
, and
Zhu
,
Q.
,
2016
, “
Acoustic Emission-Based Condition Monitoring Methods: Review and Application for Low Speed Slew Bearing
,”
Mech. Syst. Sig. Process.
,
72–73
(
May
), pp.
134
159
.
12.
Aguiar
,
P. R.
,
Martins
,
C. H. R.
,
Marchi
,
M.
, and
Bianchi
,
E. C.
,
2012
, “
Digital Signal Processing for Acoustic Emission
,”
Data Acquisition Appl.
13.
Kim
,
Y. H.
,
Tan
,
A. C. C.
, and
Yang
,
B. S.
,
2008
, “
Parameters Comparison of Acoustic Emission Signals for Condition Monitoring of Low-Speed Bearings
,”
Aust. J. Mech. Eng.
,
6
(
1
), pp.
45
52
.
14.
Kaewwaewnoi
,
W.
,
Prateepasen
,
A.
, and
Kaewtrakulpong
,
P.
,
2005
,
Measurement of Valve Leakage Rate Using Acoustic Emission
,
King Mongkut’s University of Technology Thonburi
,
Thailand
.
15.
Jamaludin
,
N.
, and
Mba
,
D.
,
2002
, “
Monitoring Extremely Slow Rolling Element Bearings: Part I
,”
NDT E Int.
,
35
(
6
), pp.
349
358
.
16.
Jamaludin
,
N.
, and
Mba
,
D.
,
2002
, “
Monitoring Extremely Slow Rolling Element Bearings: Part II
,”
NDT E Int.
,
35
(
6
), pp.
359
366
.
17.
Al-Balushi
,
K. R.
,
Addali
,
A.
,
Chamley
,
B.
, and
Mba
,
D.
,
2010
, “
Energy Index Technique for Detection of Acoustic Emissions Associated With Incipient Bearing Failures
,”
Appl. Acoust.
,
71
(
9
), pp.
812
821
.
18.
He
,
P.
,
Li
,
P.
, and
Sun
,
H.
,
2011
, “
Feature Extraction of Acoustic Signals Based on Complex Morlet Wavelet
,”
Procedia Engineering
,
15
, pp.
464
468
.
19.
Li
,
C. J.
, and
Li
,
S. Y.
,
1995
, “
Acoustic Emission Analysis for Bearing Condition Monitoring
,”
Wear
,
185
(
1–2
), pp.
67
74
.
20.
Kim
,
B. S.
,
Gu
,
D. S.
,
Kim
,
J. G.
,
Kim
,
Y. C.
, and
Choi
,
B. K.
,
2009
, “
Rolling Element Bearing Fault Detection using Acoustic Emission Signal Analyzed by Envelope Analysis with Discrete Wavelet Transform
,”
Proceedings of the 4th World Conference on Engineering Asset Lifecycle Management
,
Greece
,
Sept. 28–30
, pp.
596
602
.
21.
Niknam
,
S. A.
,
Thomas
,
T.
,
Hines
,
J. W.
, and
Sawhney
,
R.
,
2013
, “
Analysis of Acoustic Emission Data for Bearings Subject to Unbalance
,”
Int. J. Prog. Health Manage.
,
4
(
15
), pp.
80
89
.
22.
Oh
,
H.
,
Azarian
,
M. H.
, and
Pecht
,
M.
,
2011
, “
Estimation of Fan Bearing Degradation using Acoustic Emission Analysis and Mahalanobis Distance
,”
Proceedings of the Technical Program for MFPT: The Applied Systems Health Management Conference
,
University of Maryland, College Park, MD
.
23.
Niknam
,
S. A.
,
Songmene
,
V.
, and
Au
,
Y. H. J.
,
2013
,
Proposing a New Acoustic Emission Parameter for Bearing Condition Monitoring in Rotating Machines
, CSME-87 No. 12, E.I.C. Accession 3407.
24.
Liu
,
R.
,
Yang
,
B.
,
Zio
,
E.
, and
Chen
,
X.
,
2018
, “
Artificial Intelligence for Fault Diagnosis of Rotating Machinery: A Review
,”
Mech. Syst. Sig. Process.
,
108
(
Aug.
), pp.
33
47
.
25.
Jia
,
F.
,
Lei
,
Y.
,
Lin
,
J.
,
Zhou
,
X.
, and
Lu
,
N.
,
2016
, “
Deep Neural Networks: A Promising Tool for Fault Characteristic Mining and Intelligent Diagnosis of Rotating Machinery With Massive Data
,”
Mech. Syst. Sig. Process.
,
72–73
(
May
), pp.
303
315
.
26.
Lei
,
Y.
,
He
,
Z.
,
Zi
,
Y.
, and
Hu
,
Q.
,
2008
, “
Fault Diagnosis of Rotating Machinery Based on a New Hybrid Clustering Algorithm
,”
Int. J. Adv. Manuf. Technol.
,
35
(
9–10
), pp.
968
977
.
27.
Jardine
,
A. K. S.
,
Lin
,
D.
, and
Banjevic
,
D.
,
2006
, “
A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance
,”
Mech. Syst. Sig. Process.
,
20
(
7
), pp.
1483
1510
.
28.
Sikorska
,
J. Z.
, and
Mba
,
D.
,
2008
, “
Challenges and Obstacles in the Application of Acoustic Emission to Process Machinery
,”
Proc. Inst. Mech. Eng., Part E: J. Process Mech. Eng.
,
222
(
1
), pp.
1
19
.
29.
Sikorska
,
J. Z.
,
2006
, “
The Application of Acoustic Emission Monitoring to the Detection of Flow Conditions in Centrifugal Pumps
,” Ph.D. thesis,
University of WA
.
30.
Mossing
,
J. C.
, and
Tuthill
,
T. A.
,
1996
, “
Reduced Interference Distributions for the Detection and Classification of Outside Sound Source Acoustic Emissions
,”
IEEE International Conference on Acoustics, Speech and Signal Processing
,
Atlanta, GA
, pp.
2758
2761
.
31.
Wu
,
J. D.
, and
Chen
,
J. C.
,
2006
, “
Continuous Wavelet Transform Technique for Fault Signal Diagnosis of Internal Combustion Engines
,”
NDT E Int.
,
39
(
4
), pp.
304
311
.
32.
Jawadekar
,
A.
,
Paraskar
,
S.
,
Jadhav
,
S.
, and
Dhole
,
G.
,
2014
, “
Artificial Neural Network Based Induction Motor Fault Classifier Using Continuous Wavelet Transform
,”
Syst. Sci. Control Eng.
,
2
(
1
), pp.
684
690
.
33.
Jardine
,
A. K. S.
,
Lin
,
D.
, and
Banjevic
,
D.
,
2006
, “
Wavelets for Fault Diagnosis of Rotary Machines, A Review With Applications
,”
Mech. Syst. Sig. Process.
,
20
(
7
), pp.
1483
1510
.
34.
Antoni
,
J.
,
2009
, “
Cyclostationarity by Examples
,”
Mech. Syst. Sig. Process.
,
23
(
4
), pp.
987
1036
.
35.
Leon-Garcia
,
A.
,
2008
,
Probability, Statistics, and Random Processes for Electrical Engineering
,
3rd ed.
,
Pearson Prentice Hall
,
Upper Saddle River, NJ
.
36.
Tse
,
P. W.
,
Yang
,
W. X.
, and
Tam
,
H. Y.
,
2004
, “
Machine Fault Diagnosis Through An Effective Exact Wavelet Analysis
,”
J. Sound Vib.
,
277
(
4–5
), pp.
1005
1024
.
37.
Poularikas
,
A. D.
,
2010
,
The Transforms and Applications Handbook
,
3rd ed.
,
CRC Press
,
Boca Raton, FL
.
38.
Law
,
L. S.
,
Kim
,
J. H.
,
Liew
,
Willey Y. H.
, and
Lee
,
S. K.
,
2012
, “
An Approach Based on Wavelet Packet Decomposition and Hilbert-Huang Transform (WPD HHT) for Spindle Bearings Condition Monitoring
,”
Mech. Syst. Sig. Process.
,
33
(
Nov.
), pp.
197
211
.
39.
Peng
,
Z. K.
,
Tse
,
P. W.
, and
Chu
,
F. L.
,
2005
, “
An Improved Hilbert-Huang Transform and Its Application in Vibration Signal Analysis
,”
J. Sound Vib.
,
286
(
1–2
), pp.
187
205
.
40.
Huang
,
N. E.
,
Shen
,
Z.
,
Long
,
S. R.
,
Wu
,
M.
,
Shih
,
H.
,
Zheng
,
N.
,
Yen
,
C.
,
Tung
,
C. C.
, and
Liu
,
H. H.
,
1998
, “
The Empirical Mode Decomposition and the Hilbert Spectrum for Non-linear and Nonstationary Time Series Analysis
,”
The Proceedings of the Royal Society A, Mathematical, Physical, and Engineering Sciences
.
41.
Eckmann
,
J. P.
, and
Ruelle
,
D.
,
1985
, “
Ergodic Theory of Chaos and Strange Attractors
,”
Rev. Mod. Phys.
,
57
(
3
), pp.
617
656
.
42.
Wolf
,
A.
,
Swift
,
J. B.
,
Swinney
,
H. L.
, and
Vastano
,
J. A.
,
1985
, “
Determining Lyapunov Exponents From a Time Series
,”
Phys. D
,
16
(
3
), pp.
285
317
.
43.
Pai
,
P. F.
,
2007
, “
Nonlinear Vibration Characterization by Signal Decomposition
,”
J. Sound Vib.
,
307
(
3–5
), pp.
527
544
.
44.
Khalil
,
H. K.
,
2002
,
Nonlinear Systems
,
3rd ed.
,
Prentice Hall
,
Upper Saddle River, NJ
.
45.
Caesarendra
,
W.
,
Kosasih
,
B.
,
Tieu
,
A. K.
, and
Moodie
,
C. A. S.
,
2015
, “
Application of the Largest Lyapunov Exponent Algorithm for Feature Extraction in Low Speed Slew Bearing Condition Monitoring
,”
Mech. Syst. Sig. Process.
,
50–51
(
Jan.
), pp.
116
138
.
46.
Kedadouche
,
M.
,
Thomas
,
M.
,
Tahan
,
A.
, and
Guilbault
,
R.
,
2014
, “
Monitoring Gears by Vibration Measurements: Lempel-Ziv Complexity and Approximate Entropy as Diagnostic Tools
,”
Proceedings of the Analyse Vibratoire Expérimentale (AVE)
,
Blois, France
,
Nov. 2014
.
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