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Technical Briefs

Symbolic Dynamic Analysis of Transient Time Series for Fault Detection in Gas Turbine Engines

[+] Author and Article Information
Soumalya Sarkar

e-mail: svs5464@psu.edu

Kushal Mukherjee

Mem. ASME e-mail: kushal.mukherjee@gmail.com

Soumik Sarkar

Mem. ASME e-mail: sarkars@utrc.utc.com

Asok Ray

Fellow ASME e-mail: axr2@psu.eduDepartment of Mechanical Engineering, The Pennsylvania State University, University Park, PA 16802

1Present address: United Technologies Research Center, Cork, Ireland.

2Present address: United Technologies Research Center, East Hartford, USA.

Contributed by the Dynamic Systems Division of ASME for publication in the Journal of Dynamic Systems, Measurement, and Control. Manuscript received October 11, 2011; final manuscript received July 27, 2012; published online November 7, 2012. Assoc. Editor: Eugenio Schuster.

J. Dyn. Sys., Meas., Control 135(1), 014506 (Nov 07, 2012) (6 pages) Paper No: DS-11-1309; doi: 10.1115/1.4007699 History: Received October 11, 2011; Revised July 27, 2012

This brief paper presents a symbolic dynamics-based method for detection of incipient faults in gas turbine engines. The underlying algorithms for fault detection and classification are built upon the recently reported work on symbolic dynamic filtering. In particular, Markov model-based analysis of quasi-stationary steady-state time series is extended to analysis of transient time series during takeoff. The algorithms have been validated by simulation on the NASA Commercial Modular Aero Propulsion System Simulation (C-MAPSS) transient test-case generator.

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References

Lipowsky, H., Staudacher, S., Bauer, M., and Schmidt, K.-J., 2010, “Application of Bayesian Forecasting to Change Detection and Prognosis of Gas Turbine Performance,” ASME J. Eng. Gas Turbines Power, 132(3), p. 031602. [CrossRef]
Guruprakash, V., and Ganguli, R., 2008, “Three-and Seven-Point Optimally Weighted Recursive Median Filters for Gas Turbine Diagnostics,” Proc. Inst. Mech. Eng., Part G: J. Aerosp. Eng., 222(3), pp. 307–318. [CrossRef]
Li, Y. G., 2003, “A Gas Turbine Diagnostic Approach With Transient Measurements,” Proc. Inst. Mech. Eng., Part A, 217(2), pp. 169–177. [CrossRef]
Merrington, G., Kwon, O., Goodwin, G., and Carlsson, B., 1991, “Fault Detection and Diagnosis in Gas Turbines,” ASME J. Eng. Gas Turbines Power, 113, pp. 276–282. [CrossRef]
Wang, X., McDowell, N., Kruger, U., McCullough, G., and Irwin, G. W., 2008, “Semi-Physical Neural Network Model in Detecting Engine Transient Faults Using the Local Approach,” Proceedings of the 17th World Congress of the International Federation of Automatic Control (IFAC'08), July 6–11. [CrossRef]
Surender, V. P., and Ganguli, R., 2005, “Adaptive Myriad Filter for Improved Gas Turbine Condition Monitoring Using Transient Data,” ASME J. Eng. Gas Turbines Power, 127, pp. 329–339. [CrossRef]
Menon, S., Uluyol, O., Kim, K., and Nwadiogbu, E. O., 2003, “Incipient Fault Detection and Diagnosis in Turbine Engines Using Hidden Markov Models,” ASME Turbo Expo, Paper No. GT2003-38589, pp. 493–500. [CrossRef]
Ray, A., 2004, “Symbolic Dynamic Analysis of Complex Systems for Anomaly Detection,” Signal Process., 84(7), pp. 1115–1130. [CrossRef]
Rao, C., Ray, A., Sarkar, S., and Yasar, M., 2009, “Review and Comparative Evaluation of Symbolic Dynamic Filtering for Detection of Anomaly Patterns,” Signal, Image Video Process., 3(2), pp. 101–114. [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. Inst. Mech. Eng., Part G: J. Aerosp. Eng., 222(3), pp. 307–318. [CrossRef]
Sarkar, S., Yasar, M., Gupta, S., Ray, A., and Mukherjee, K., 2008, “Fault Detection and Isolation in Aircraft Gas Turbine Engines. Part 2: Validation on a Simulation Test Bed,” Proc. Inst. Mech. Eng., Part G: J. Aerosp. Eng., 222(3), pp. 319–330. [CrossRef]
Sarkar, S., Rao, C., and Ray, A., 2009, “Statistical Estimation of Multiple Faults in Aircraft Gas Turbine Engines,” Proc. Inst. Mech. Eng., Part G: J. Aerosp. Eng., 223(4), pp. 415–424. [CrossRef]
Sarkar, S., Jin, X., and Ray, A., 2011, “Data-Driven Fault Detection in Aircraft Engines With Noisy Sensor Measurements,” ASME J. Eng. Gas Turbines Power, 133(8), p. 081602. [CrossRef]
Armstrong, J., 2009, “User’s Guide for the Transient Test Case Generator,” NASA GRC Internal Report.
Frederick, D. K., DeCastro, J. A., and Litt, J. S., 2007, “User’s Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS),” Report No. NASA/TM2007-215026.
Rajagopalan, V., and Ray, A., 2006, “Symbolic Time Series Analysis via Wavelet-Based Partitioning,” Signal Process., 86(11), pp. 3309–3320. [CrossRef]
Wen, Y., and Ray, A., 2012, “Vector Space Formulation of Probabilistic Finite State Automata,” J. Comput. Syst. Sci., 78, pp. 1127–1141. [CrossRef]
Adenis, P., Wen, Y., and Ray, A., 2012, “An Inner Product Space on Irreducible and Synchronizable Probabilistic Finite State Automata,” Math. Control, Signals, Syst., 23(1), pp. 281–310. [CrossRef]
Ferguson, T., 1973, “Bayesian Analysis of Some Nonparametric Problems,” Ann. Stat., 1(2), pp. 209–230. [CrossRef]
Sethuraman, J., 1994, “A Constructive Definition of Dirichlet Priors,” Stat. Sin., 4, pp. 639–650.
Wilks, S., 1963, Mathematical Statistics, John Wiley, New York.
Pathria, R., 1996, Statistical Mechanics, 2nd ed., Butterworth-Heinemann, Oxford, UK.
Kobayashi, T., and Simon, D., 2005, “Hybrid Neural-Network Genetic-Algorithm Technique for Aircraft Engine Performance Diagnostics,” J. Propulsion Power, 21(4), pp. 751–758. [CrossRef]
Poor, V., 1988, An Introduction to Signal Detection and Estimation, 2nd ed., Springer-Verlag, New York.
Sarkar, S., Singh, D. S., Srivastav, A., and Ray, A., 2011, “Semantic Sensor Fusion for Fault Diagnosis in Aircraft Gas Turbine Engines,” Proceedings of American Control Conference, San Francisco, CA.

Figures

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Fig. 1

Schematic diagram of the C-MAPSS engine model with sensors

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Fig. 2

Detection in a multifault framework (ground truth: a faulty LPC)

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Fig. 3

High-level fan fault detection (ground truth: a high-level fan fault)

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Fig. 4

Low-level fan fault detection (ground truth: a low-level fan fault)

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Fig. 5

ROC curves for fan fault identification with different test data lengths (λ varying between 0.01 and 2 with steps of 0.01)

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