The aero-engine gas-path electrostatic monitoring system is capable of providing early warning of impending gas-path component faults. In the presented work, a method is proposed to acquire signal sample under a specific operating condition for on-line fault detection. The symbolic time-series analysis (STSA) method is adopted for the analysis of signal sample. Advantages of the proposed method include its efficiency in numerical computations and being less sensitive to measurement noise, which is suitable for in situ engine health monitoring application. A case study is carried out on a data set acquired during a turbojet engine reliability test program. It is found that the proposed symbolic analysis techniques can be used to characterize the statistical patterns presented in the gas path electrostatic monitoring data (GPEMD) for different health conditions. The proposed anomaly measure, i.e., the relative entropy derived from the statistical patterns, is confirmed to be able to indicate the gas path components faults. Finally, the further research task and direction are discussed.

References

References
1.
Fisher
,
C. E.
,
2000
, “
Gas Path Debris Monitoring—A 21st Century PHM Tool
,”
IEEE Aerospace Conference Proceedings
, Big Sky, MT, Mar. 25, Vol.
6
, pp.
441
448
.
2.
Wen
,
Z. H.
,
Zuo
,
H. F.
, and
Pecht
,
G. P.
,
2011
, “
Electrostatic Monitoring of Gas Path Debris for Aero-Engines
,”
IEEE Trans. Reliab.
,
60
(
1
), pp.
33
40
.
3.
Vatazhin
,
A. B.
,
Golentsov
,
D. A.
,
Likhter
,
V. A.
, and
Shulgin
,
V. I.
1997
, “
Noncontact Electrostatic Engine Diagnostics: Theoretical and Laboratory Simulation
,”
Fluid Dyn.
,
32
, pp.
223
232
.
4.
Powrie
,
H.
,
Mcnicholas
,
K.
,
Powrie
,
H.
, and
McNicholas
,
K.
,
1997
, “
Gas Path Monitoring During Accelerated Mission Testing of a Demonstrator Engine
,”
AIAA
Paper No. 97-2904.
5.
Sun
,
J. Z.
,
Zuo
,
H. F.
,
Liu
,
P. P.
, and
Wen
,
Z. H.
,
2013
, “
Experimental Study on Engine Gas-Path Component Fault Monitoring Using Exhaust Gas Electrostatic Signal
,”
Meas. Sci. Technol.
,
24
(
12
), pp.
5107
5117
.
6.
Liu
,
P. P.
,
Zuo
,
H. F.
, and
Sun
,
J. Z.
,
2014
, “
The Electrostatic Sensor Applied to the Online Monitoring Experiments of Combustor Carbon Deposition Fault in Aero-Engine
,”
IEEE Sens. J.
,
14
(
3
), pp.
686
694
.
7.
Powrie
,
H. E. G.
, and
Fisher
,
C. E.
,
1999
, “
Engine Health Monitoring: Towards Total Prognostics
,”
IEEE Aerospace Applications Conference Proceedings
, Aspen, CO, Mar. 7, Vol.
3
, pp.
11
20
.
8.
Powrie
,
H. E.
, and
Novis
,
A.
,
2006
, “
Gas Path Debris Monitoring for F-35 Joint Strike Fighter Propulsion System PHM
,”
IEEE Aerospace Conference
, Big Sky, MT, Mar. 4–11, Vol.
2
, pp.
1
8
.
9.
Sun
,
J. Z.
,
Zuo
,
H. F.
,
Zhan
,
Z. J.
, and
Liu
,
P. P.
,
2012
, “
Analysis of the Influencing Factors on the Exhaust Gas Electrostatic Monitoring Signal of a Turbo-Shaft Engine
,”
Acta Aeronaut. Astron. Sin.
,
33
(
3
), pp.
412
420
.
10.
Ray
,
A.
,
2004
, “
Symbolic Dynamic Analysis of Complex Systems for Anomaly Detection
,”
Signal Process.
,
84
(
7
), pp.
1115
1130
.
11.
Gupta
,
S.
,
Ray
,
A.
,
Sarkar
,
S.
, and
Yasar
,
M.
,
2008
, “
Fault Detection and Isolation in Aircraft Gas Turbine Engines—Part 1: Underlying Concept
,”
Proc. IMechE Part G: J. Aerosp. Eng.
,
222
(
3
), pp.
307
317
.
12.
Li
,
Y.
,
Chattopadhyay
,
P.
, and
Ray
,
A.
,
2015
, “
Dynamic Data-Driven Identification of Battery State-of-Charge via Symbolic Analysis of Input-Output Pairs
,”
J. Appl. Energy
,
155
, pp.
778
790
.
13.
Daw
,
C. S.
,
Finney
,
C. E. A.
, and
Tracy
,
E. R.
,
2003
, “
A Review of Symbolic Analysis of Experimental Data
,”
Rev. Sci. Instrum.
,
74
(
2
), pp.
915
930
.
14.
Sarkar
,
S.
,
Ray
,
A.
,
Mukhopadhyay
,
A.
, and
Sen
,
S.
,
2015
, “
Dynamic Data-Driven Prediction of Lean Blowout in a Swirl-Stabilized Combustor
,”
Int. J. Spray Combust. Dyn.
,
7
(
3
), pp.
209
242
.
15.
Sarkar
,
S.
,
Chakravarthy
,
S. R.
,
Ramanan
,
V.
, and
Ray
,
A.
,
2016
, “
Dynamic Data-Driven Prediction of Instability in a Swirl-Stabilized Combustor
,”
Int. J. Spray Combust. Dyn.
,
8
(
4
), pp.
235
253
.
16.
Coombes
,
J. R.
, and
Yan
,
Y.
,
2015
, “
Experimental Investigations Into the Flow Characteristics of Pneumatically Conveyed Biomass Particles Using an Electrostatic Sensor Array
,”
Fuel
,
151
, pp.
11
20
.
17.
Zhang
,
W.
,
Wang
,
C.
,
Yang
,
W.
, and
Wang
,
C. H.
,
2014
, “
Application of Electrical Capacitance Tomography in Particulate Process Measurement—A Review
,”
Adv. Powder Technol.
,
25
(
1
), pp.
174
188
.
18.
Xu
,
C.
,
Li
,
J.
,
Gao
,
H.
, and
Wang
,
S.
,
2012
, “
Investigations Into Sensing Characteristics of Electrostatic Sensor Arrays Through Computational Modelling and Practical Experimentation
,”
J. Electrost.
,
70
(
1
), pp.
60
71
.
19.
Addabbo
,
T.
,
Fort
,
A.
,
Garbin
,
R.
,
Mugnaini
,
M.
,
Rocchi
,
S.
, and
Vignoli
,
V.
,
2015
, “
Theoretical Characterization of a Gas Path Debris Detection Monitoring System Based on Electrostatic Sensors and Charge Amplifiers
,”
Measurement
,
64
, pp.
138
146
.
20.
Addabbo
,
T.
,
Fort
,
A.
,
Mugnaini
,
M.
,
Panzardi
,
E.
, and
Vignoli
,
V.
,
2016
, “
A Smart Measurement System With Improved Low-Frequency Response to Detect Moving Charged Debris
,”
IEEE Trans. Instrum. Meas.
,
65
(
8
), pp.
1874
1883
.
21.
Wen
,
Z. H.
,
Hou
,
J. X.
, and
Jiang
,
Z. Q.
,
2015
, “
Formation Mechanism Analysis and Detection of Charged Particles in an Aero-Engine Gas Path
,”
Int. J. Aeronaut. Space Sci.
,
16
(
2
), pp.
247
253
.
22.
Rajagopalan
,
V.
, and
Ray
,
A.
,
2005
, “
Wavelet-Based Space Partitioning for Symbolic Time Series Analysis
,” IEEE Conference on Decision and Control and European Control Conference (
CDC-ECC'05
), Seville, Spain, Dec. 12–15, pp.
5245
5250
.
23.
Kennel
,
M. B.
, and
Mees
,
A. I.
,
2000
, “
Testing for General Dynamical Stationarity With a Symbolic Data Compression Technique
,”
Phys. Rev. E: Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top.
,
61
(
3
), pp.
2563
2568
.
24.
Alamdari
,
M. M.
,
Samali
,
B.
, and
Li
,
J.
,
2015
, “
Damage Localization Based on Symbolic Time Series Analysis
,”
Struct. Control Health Monit.
,
22
(
2
), pp.
374
393
.
25.
Tang
,
X. Z.
,
Tracy
,
E. R.
, and
Brown
,
R.
,
1997
, “
Symbol Statistics and Spatio-Temporal Systems
,”
Phys. D: Nonlinear Phenom.
,
102
(
3–4
), pp.
253
261
.
You do not currently have access to this content.