0
Research Papers: Gas Turbines: Turbomachinery

Symbolic Time-Series Analysis of Gas Turbine Gas Path Electrostatic Monitoring Data

[+] Author and Article Information
Jianzhong Sun

College of Civil Aviation,
Nanjing University of Aeronautics
and Astronautics,
Nanjing 211106, China
e-mail: sunjianzhong@nuaa.edu.cn

Pengpeng Liu

System Engineering Research Institute,
China State Shipbuilding Corporation,
Beijing 100036, China
e-mail: liutianyu221@163.com

Yibing Yin

College of Civil Aviation,
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: yinyibing1992@163.com

Hongfu Zuo

College of Civil Aviation,
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: rms@nuaa.edu.cn

Chaoyi Li

College of Civil Aviation,
Nanjing University of Aeronautics and Astronautics,
Nanjing 211106, China
e-mail: lichaoyi_nuaa@163.com

Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received September 14, 2016; final manuscript received March 22, 2017; published online May 9, 2017. Assoc. Editor: Allan Volponi.

J. Eng. Gas Turbines Power 139(10), 102603 (May 09, 2017) (7 pages) Paper No: GTP-16-1450; doi: 10.1115/1.4036492 History: Received September 14, 2016; Revised March 22, 2017

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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

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.
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. [CrossRef]
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.
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.
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.
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. [CrossRef]
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.
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.
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.
Ray, A. , 2004, “ Symbolic Dynamic Analysis of Complex Systems for Anomaly Detection,” Signal Process., 84(7), pp. 1115–1130. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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.
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.
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.
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. [CrossRef]
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. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Principle of the engine gas path electrostatic monitoring

Grahic Jump Location
Fig. 2

Charged particle(s) passing along the sensor probe

Grahic Jump Location
Fig. 3

The equivalent measurement circuit of the electrostatic monitoring system

Grahic Jump Location
Fig. 4

The gas path electrostatic monitoring system

Grahic Jump Location
Fig. 5

Experiment setup on turbojet engine

Grahic Jump Location
Fig. 6

GPEMD of one test cycle

Grahic Jump Location
Fig. 7

A sample signal under a specific operating condition (nominal state)

Grahic Jump Location
Fig. 8

Sample signals under a specific operating condition (anomalous state)

Grahic Jump Location
Fig. 9

Symbol sequence histogram from the GPEMD in the nominal state

Grahic Jump Location
Fig. 10

Anomaly measure calculated from GPEMD using the symbolic analysis method

Grahic Jump Location
Fig. 11

The relative entropy against the oil consumption

Grahic Jump Location
Fig. 12

The symbol-sequence histogram from the GPEMD at 10/13

Grahic Jump Location
Fig. 13

The symbol-sequence histograms from the GPEMD at 11/24

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In