0
Research Papers: Gas Turbines: Aircraft Engine

An Approach for Optimal Measurements Selection on Gas Turbine Engine Fault Diagnosis

[+] Author and Article Information
Min Chen

School of Energy and Power Engineering,
Beihang University,
Beijing 100191, China
e-mail: chenmin@buaa.edu.cn

Liang Quan Hu

Collaborative Innovation Center of Advanced
Aero-Engine,
Beijing 100191, China
e-mail: lovehuliangquan@163.com

Hailong Tang

School of Energy and Power Engineering,
Beihang University,
Beijing 100191, China
e-mail: 75249612@qq.com

1Corresponding author.

Contributed by the Aircraft Engine Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received September 1, 2014; final manuscript received November 8, 2014; published online December 23, 2014. Editor: David Wisler.

J. Eng. Gas Turbines Power 137(7), 071203 (Jul 01, 2015) (9 pages) Paper No: GTP-14-1523; doi: 10.1115/1.4029171 History: Received September 01, 2014; Revised November 08, 2014; Online December 23, 2014

Gas path fault diagnosis plays an important role in guaranteeing safe, reliable and cost-effective operation for gas turbine engines. Measurements selection is among the most critical issues for diagnostic method implementation. In this paper, an integration approach for optimal measurements selection, which combines finger print diagrams analysis, health parameters correlation analysis, performance estimation uncertainty index analysis and fault cases validation based on genetic algorithm, has been proposed and applied to assess the health condition of a two-spool split flow turbofan in test bed. First, mathematical description of an engine gas path fault diagnosis process was given and the influence coefficient matrix was also calculated based on a well calibrated nonlinear engine performance simulation model. Second, the number of combination candidates was reduced from 782 to 256 and three measurements were picked out using the finger print diagrams analysis and the health parameters correlation analysis. Then, the number of the combination candidates was further narrowed down to 13 using the performance estimation uncertainty index analysis. A nonlinear genetic algorithm fault diagnosis method was applied to test the diagnostic ability of the remaining measurement candidates. Finally, an optimal measurement combination was worked out which demonstrated the effectiveness of the integration approach. This integration approach for optimal measurements selection is also applicable to other type of gas turbine engines.

FIGURES IN THIS ARTICLE
<>
Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.

References

Li, Y. G., 2002, “Performance-Analysis-Based Gas Turbine Diagnosis: A Review,” Proc. Inst. Mech. Eng., Part A, 216(A6), pp. 363–377. [CrossRef]
Doel, D. L., 1993, “Gas Path Analysis-Problem and Solution,” 17th Symposium of Aircraft Integrated Monitoring Systems, Bonn, Germany, Sept. 21–23, pp. 21–23.
Volponi, A. J., 1982, “Gas Path Analysis: An Approach to Engine Diagnostics,” 35th Symposium Mechanical Failures Prevention Group, Gaithersburg, MD, Apr. 20–22, pp. 10–18.
Provost, M. J., 1995, “The Use of Optimal Estimation Techniques in the Analysis of Gas Turbine,” Ph.D. thesis, Cranfield University, Cranfield, UK.
Stamatis, M., Mathioudakis, K., and Papailiou, K., 1992, “Optimal Measurement and Health Index Selection for Gas Turbine Performance Status and Fault Diagnosis,” ASME J. Gas Turbines Power, 114(2), pp. 209–216. [CrossRef]
Zedda, M., and Singh, R., 1999, “Gas Turbine Engine and Sensor Fault Diagnosis Using Optimization Techniques,” AIAA Paper No. 99-2530 [CrossRef].
Mathioudakis, K., and Kamboukos, P., 2006, “Assessment of the Effectiveness of Gas Path Diagnosis Schemes,” ASME J. Gas Turbines Power, 128(1), pp. 57–63. [CrossRef]
Kamboukos, P., Oikonomou, P., Stamatis, A., and Mathioudakis, K., 2001, “Optimizing Diagnostic Effectiveness of Mixed Turbofans By Means of Adaptive Modeling and Choice of Appropriate Monitoring Parameters,” AVT Symposium on Monitoring and Management of Gas Turbine Fleets for Extended Life and Reduced Costs, Manchester, UK, Oct. 8–11, pp. 9-1–9-13.
Provost, M. J., 2003, Observability Analysis for Successful Diagnosis of Gas Turbine Faults (VKI Lecture Series—Gas Turbine Condition Monitoring and Fault Diagnosis), von Karman Institute, Sint-Genesius-Rhode, Belgium.
Jasmani, M. S., Li, Y. G., and Ariffin, Z., 2010, “Measurement Selection for Multi-Component Gas Path Diagnostics Using Analytical Approach and Measurement Subset Concept,” ASME Paper No. GT2010-22402 [CrossRef].
Mushini, R., and Simon, D., 2005, “On Optimization of Sensor Selection for Aircraft Gas Turbine Engines,” 18th International Conference on Systems Engineering (ICSEng 2005), Washington, DC, Aug. 16–18 [CrossRef].
Borguet, S., and Leonard, O., 2008, “The Fisher Information Matrix as a Relevant Tool for Sensor Selection in Engine Health Monitoring,” Int. J. Rotating Mach., 2008(1), p. 784749. [CrossRef]
Santi, L., Sowers, T., and Aguilar, B., 2005, “Optimal Sensor Selection for Health Monitoring Systems,” AIAA Paper No. 2005-4485. [CrossRef]
Borguet, S., and Leonard, O., 2010, “A Sparse Estimation Approach to Fault Isolation,” ASME J. Gas Turbines Power, 132(2), p. 021601. [CrossRef]
Litt, J., 2008, “An Optimal Orthogonal Decomposition Method for Kalman Filter-Based Turbofan Engine Thrust Estimation,” ASME J. Gas Turbines Power, 130(1), p. 011601 [CrossRef].
Tang, H. L., and Zhang, J., 1999, “A Study of Object-Oriented Approach for Aero-Engine Performance Simulation,” J. Aerosp. Power, 14(4), pp. 421–424.
Chen, M., Tang, H. L., and Zhang, K., 2012, “Turbine Based Combined Cycle Propulsion System Integration Concept Design,” Proc. Inst. Mech. Eng., Part G, 227(7), pp. 1068–1089. [CrossRef]
Pinelli, M., and Spina, P., 2002, “Gas Turbine Field Performance Determination: Sources of Uncertainties,” ASME J. Gas Turbines Power, 124(1), pp. 155–160. [CrossRef]
Aretakis, N., Mathioudakis, K., and Stamatis, A., 2003, “Non-Linear Engine Component Fault Diagnosis From a Limited Number of Measurements Using a Combinatorial Approach,” ASME J. Gas Turbines Power, 125(4). p. 041701. [CrossRef]
Golub, H. G., and Van, C. F., 1996, Matrix Computations, 3rd ed., John Hopkins University Press, Baltimore, MD.
Stamatis, A., Mathioudakis, K., Berios, G., and Papailiou, K. D., 1989, “Jet Engine Fault Detection With Discrete Operating Points Gas Path Analysis,” 9th International Symposium on Air Breathing Engines, Athens, Sept. 3–8, ISABE Paper No. 89-7133.
Srinivas, M., and Patnaik, L. M., 1994, “Adaptive Probabilities of Crossover and Mutations in Gas,” IEEE Trans. SMC, 24(4), pp. 656–667.
Li, Y. G., 2008, “An Genetic Algorithm Approach to Estimate Performance Status of Gas Turbine,” ASME Paper No. ASME GT2008-50175 [CrossRef].
Gulati, A., Zedda, M., and Singh, R., 2000, “Gas Turbine Engine and Sensor Multiple Operating Point Analysis Using Optimization Techniques,” AIAA Paper No. 2000-3716 [CrossRef].
Li, Y. G., and Nilkitsaranont, P., 2009, “Gas Turbine Performance Prognostic for Condition-Based Maintenance,” Appl. Energy, 86(1), pp. 2152–2161. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Two-spool split flow turbofan structure diagram

Grahic Jump Location
Fig. 2

Flow chart of measurements selection process

Grahic Jump Location
Fig. 3

Total air flow rate (WA) finger print diagram

Grahic Jump Location
Fig. 4

Correlation analysis diagram between E1 and E4

Grahic Jump Location
Fig. 5

Correlation analysis diagram between E2 and E3

Grahic Jump Location
Fig. 6

Measurement combinations validation using genetic algorithm

Grahic Jump Location
Fig. 7

Single component fault identification results

Grahic Jump Location
Fig. 8

Two components faults identification results

Grahic Jump Location
Fig. 9

Three components faults identification results

Grahic Jump Location
Fig. 10

Four components faults identification results

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