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
Your Session has timed out. Please sign back in to continue.


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.


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




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