Determining Steady-State Operation Criteria Using Transient Performance Modelling and Steady-State Diagnostics
Source abstract
Abstract
Data from the steady-state operation of gas turbine engines are used in gas path diagnostic procedures. A method to identify steady-state operation is thus required. This paper initially explains and demonstrates the factors that cause a deviation in engine health when transient data are used for diagnosis and shows that there is a threshold in the slope of time traces, below which the variation in engine health parameters is acceptable. A methodology for deriving a criterion for steady-state operation based on actual flight data is then presented. The slope of the exhaust gas temperature variation with time and the size of its time-series window, from which this slope is determined, are the required parameters that must be specified when applying this criterion. It is found that the values of these parameters must be selected so that a sufficient number of steady-state points are available without compromising the accuracy of the diagnostic procedure.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Combustion Engine Technologies · Advanced Sensor Technologies Research · Turbomachinery Performance and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Laboratory of Thermal Turbomachines, School of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece
Funding
Agency for Development of Defense, Republic of Korea
References (26)
Show source-provided references
1. Gas turbine gas-path fault diagnosis in power plant under transient operating condition with variable geometry compressor10.1002/ese3.1229 ↗
2. Gas turbine gas path diagnosis under transient operating conditions: A steady state performance model based local optimization approach10.1016/j.applthermaleng.2020.115025 ↗
3. A time-series turbofan engine successive fault diagnosis under both steady-state and dynamic conditions10.1016/j.energy.2022.125848 ↗
4. Performance diagnostics of gas turbines operating under transient conditions based on dynamic engine model and artificial neural networks
5. Salilew, W.M., Abdul Karim, Z.A., Lemma, T.A., Fentaye, A.D., and Kyprianidis, K.G. (2023). Three Shaft Industrial Gas Turbine Transient Performance Analysis. Sens. J., 23.10.3390/s23041767 ↗
6. Improved Engine Health Monitoring Using Full Flight Data and Companion Engine Information10.4271/2016-01-2024 ↗
7. Tang, L., Volponi, A.J., and Prihar, E. (2019, January 17–21). Extending Engine Gas Path Analysis Using Full Flight Data. Proceedings of the ASME Turbo Expo 2019, Phoenix, AZ, USA.10.1115/GT2019-90161 ↗
8. Weiss, M., Staudacher, S., Becchio, D., Keller, C., and Mathes, J. (2022). Steady-State Fault Detection with Full-Flight Data. Machines, 10.10.3390/machines10020140 ↗
9. An efficient method for on-line identification of steady state10.1016/0959-1524(95)00009-F ↗
10. Simon, D.L., and Litt, J.S. (2010, January 14–18). A Data Filter for Identifying Steady-State Operating Points in Engine Flight Data for Condition Monitoring Applications. Proceedings of the ASME Turbo Expo 2010, Glasgow, UK.10.1115/GT2010-22818 ↗
11. Davison, C.R. (2012, January 11–15). Determination of Steady State Gas Turbine Operation. Proceedings of the ASME Turbo Expo 2012, Copenhagen, Denmark.10.1115/GT2012-68632 ↗
12. Celis, C., Xavier, É., Teixeira, T., and Pinto, G.R.S. (2014, January 16–20). Steady State Detection in Industrial Gas Turbines for Condition Monitoring and Diagnostics Applications. Proceedings of the ASME Turbo Expo 2014, Düsseldorf, Germany.10.1115/GT2014-25007 ↗
13. Jet Engine Gas Path Analysis Based on Takeoff Performance Snapshots10.1115/1.4036954 ↗
14. Wang, P., Liu, K., and Tang, Z. (2019, January 3–5). Turbofan Engine Baseline Model Extraction Based on FDR Data. Proceedings of the Chinese Control And Decision Conference (CCDC), Nanchang, China.10.1109/CCDC.2019.8833387 ↗
15. Robust methodology for steady state measurements estimation based framework for a reliable long term thermal power plant operation performance monitoring10.1016/j.energy.2015.09.044 ↗
16. A steady-state detection method based on Gaussian discriminant analysis for the on-line gas turbine process10.1016/j.applthermaleng.2018.01.025 ↗
17. Application of the method of insignificant divergences to diagnose the technical aircraft gas turbine engine state under the transient-state conditions of its operation10.26467/2079-0619-2023-26-5-81-95 ↗
18. Gas path fault diagnosis for gas turbine engines with fully operating regions using mode identification and model matching10.1088/1361-6501/ac97b4 ↗
19. Stamatis, A., Mathioudakis, K., Ruiz, J., and Curnock, B. (2001, January 4–7). Real Time Engine Model Implementation for Adaptive Control & Performance Monitoring of Large Civil Turbofans. Proceedings of the ASME Turbo Expo 2001, New Orleans, LA, USA.10.1115/2001-GT-0362 ↗
20. Alexiou, A., and Mathioudakis, K. (2005, January 6–9). Development of Gas Turbine Performance Models using a Generic Simulation Tool. Proceedings of the ASME Turbo Expo 2005, Reno, NV, USA.10.1115/GT2005-68678 ↗
21. Transient system simulation for an aircraft engine using a data-driven model10.1016/j.energy.2020.117046 ↗
22. Turbofan Engine Health Assessment From Flight Data10.1115/1.4028566 ↗
23. (2024, March 22). EcosimPro|PROOSIS Modelling and Simulation Software. Available online: http://www.proosis.com/.
24. Adaptive Simulation of Gas Turbine Performance10.1115/1.2906157 ↗
25. Mathioudakis, K. Aegean Airlines: Data from an intra-European flight. Personal communication.
26. Bose, N.K., and Liang, P. (1995). Neural Network Fundamentals with Graphs, Algorithms and Applications, McGraw-Hill College. [1st ed.].