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TECHNICAL PAPERS: Gas Turbines: Controls, Diagnostics, and Instrumentation

Nonlinear Engine Component Fault Diagnosis From a Limited Number of Measurements Using a Combinatorial Approach

[+] Author and Article Information
N. Aretakis, K. Mathioudakis, A. Stamatis

Laboratory of Thermal Turbomachines, National Technical University of Athens, Iroon Polytechniou 9, Athens 15773, Greece

J. Eng. Gas Turbines Power 125(3), 642-650 (Aug 15, 2003) (9 pages) doi:10.1115/1.1582494 History: Received December 01, 2001; Revised March 01, 2002; Online August 15, 2003
Copyright © 2003 by ASME
Topics: Measurement , Engines
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References

Urban, L. A., and Volponi, A. J., 1992, “Mathematical Methods of Relative Engine Performance Diagnostics,” SAE Trans. 101 , J. Aerosp. Sect. 1, Technical Paper 922048.
Barwell, M. J., 1987, “Compass-Ground Based Engine Monitoring Program for General Applications,” SAE Technical Paper 871734.
Provost, M. J., 1988, “COMPASS: A Generalized Ground Based Monitoring System,” Paper 42 AGARD-CP-448, Engine Condition Monitoring, Technology and Experience.
Doel,  D., 1994, “TEMPER—A Gas Path Analysis Tool for Commercial Jet Engines,” ASME J. Eng. Gas Turbines Power, 116, pp. 82–89.
Doel,  D., 1994, “An Assessment of Weighted-Least-Squares Based Gas Path Analysis,” ASME J. Eng. Gas Turbines Power, 116, pp. 366–373.
Volponi, A., “Sensor Error Compensation in Engine Performance Diagnostics,” ASME Paper 94-GT-58.
Stamatis,  A., Mathioudakis,  K., Berios,  G., and Papailiou,  K., 1991, “Jet Engine Fault Detection With Discrete Operating Points Gas Path Analysis,” J. Propul., 7(6), pp. 1043–1048.
Kobayashi, T., and Simon, D., 2001, “A Hybrid Neural-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics,” 37th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, July 8–11, Salt Lake City, UT, Paper AIAA-2001-3763.
Groenstredt, T. V., 2001, “A Multi Point Gas Path Analysis Tool for Gas Turbine Engines With a Moderate Level of Instrumentation,” XV ISABE, Bangalore, India, Sept. 3–7, Paper ISABE-2001-1139.
Gulati, A., Taylor, D., and Sign, R., 2001, “Multiple Operating Point Analysis Using Genetic Algorithm Optimization for Gas Turbine Diagnostic,” XV ISABE, Bangalore, India, Sept. 3–7, paper ISABE-2001-1139.
Stamatis,  A., Mathioudakis,  K., and Papailiou,  K. D., 1990, “Adaptive Simulation of Gas Turbine Performance,” ASME J. Eng. Gas Turbines Power, 112, pg. 168–175.
Tsalavoutas, A., Mathioudakis, K., Stamatis, A., and Smith, M., 2000, “Identifying Faults in the Variable Geometry System of a Gas Turbine Compressor,” ASME Paper 2000-GT-33.
Mathioudakis,  K., Stamatis,  A., Tsalavoutas,  A., and Aretakis,  N., 2001, “Performance Analysis of Industrial Gas Turbines for Engine Condition Monitoring,” Proc. Inst. Mech. Eng., Part A, J. Power Energy, 215 (A2), pp. 173–184.
Stamatis, A., Mathioudakis, K., Ruiz, J., and Curnock, B., 2001, “Real Time Engine Model Implementation for Adaptive Control & Performance Monitoring of Large Civil Turbofans,” ASME Paper 2001-GT-0362.
Urban, L. A., 1975, “Parameter Selection for Multiple Fault Diagnostics of Gas Turbine Engines,” AGARD CP-165, Diagnostic and Engine Condition Monitoring, June Paper 19.
Kamboukos, Ph., Oikonomou, P., Stamatis, A., and Mathioudakis, K., “Optimizing Diagnostic Effectivness of Mixed Turbofans by Means of Adaptive Modelling and Choice of Appropriate Monitoring Parameters,” AVT Symposium on “Monitoring and Management of Gas Turbine Fleets for Extended Life and Reduced Costs,” Manchester, UK, 11 Oct. 8–11.
Curnock, B., 2000, “OBIDICOTE Project—Work Package 4. Steady-State Test Cases,” Rolls-Royce Report DNS62433.

Figures

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Derivation of component condition parameters using an adaptive engine model
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Layout of a turbofan engine and station numbering for positions of interest
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Health parameter deviations using a combination that (a) contains all actual fault parameters, (b) doesn’t contain all actual fault parameters
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Deviations of estimated fault parameter values using different combinations, for parameter (a) SE2 (actually affected), (b) SW26 (not affected)
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Parameter distribution calculation for SE2, data of Fig. 4(a)
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The way of producing the most probable fault signature
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Estimated fault signatures: (a) first pass, (b) second pass, noise-free data, fault L
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Estimated fault signatures: (a) fault C, (b) fault J, second pass, noise-free data
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(a) Estimated fault signature and (b) corresponding diagnostic index, second pass, noisy data, fault A
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(a) Estimated fault signature and (b) corresponding diagnostic index, second pass, noisy data, fault G
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Comparison between second pass and best fit procedure estimation results for fault A and noisy data
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Estimated fault signatures in the case of using additional measurements: (a) fault C, (b) fault J, second pass, noise-free data

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