Discrimination of Rapid and Gradual Deterioration for an Enhanced Gas Turbine Life-cycle Monitoring and Diagnostics
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Published
Apr 28, 2021
Amare Fentaye
Valentina Zaccaria
Konstantinos Kyprianidis
Abstract
Advanced engine health monitoring and diagnostic systems greatly benefit users helping them avoid potentially expensive and time-consuming repairs by proactively identifying shifts in engine performance trends and proposing optimal maintenance decisions. Engine health deterioration can manifest itself in terms of rapid and gradual performance deviations. The former is due to a fault event that results in a short-term performance shift and is usually concentrated in a single component. Whereas the latter implies a gradual performance loss that develops slowly and simultaneously in all engine components over their lifetime due to wear and tear. An effective engine life-cycle monitoring and diagnostic system is therefore required to be capable of discriminating these two deterioration mechanisms followed by isolating and identifying the rapid fault accurately. In the proposed solution, this diagnostic problem is addressed through a combination of adaptive gas path analysis and artificial neural networks. The gas path analysis is applied to predict performance trends in the form of isentropic efficiency and flow capacity residuals that provide preliminary information about the deterioration type. Sets of neural network modules are trained to filter out noise in the measurements, discriminate rapid and gradual faults, and identify the nature of the root cause, in an integrated manner with the gas path analysis. The performance of the proposed integrated method has been demonstrated and validated based on performance data obtained from a three-shaft turbofan engine. The improvement achieved by the combined approach over the gas path analysis technique alone would strengthen the relevance and long-term impact of our proposed method in the gas turbine industry.
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Keywords
Gas Turbine, Life-cycle Monitoring, Diagnostics
References
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Fentaye, A. D., & Kyprianidis, K. G. (2020). An intelligent data filtering and fault detection method for gas turbine engines. ICSC & ISATECH, 9 - 11 Oct., 2019, Amsterdam, the Netherlands. doi: https://doi.org/10.1051/matecconf/202031402007
Fentaye, A. D., Ul-Haq Gilani, S. I., Baheta, A. T., & Li, Y.-G. (2018). Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 233(6), 786-802. doi:10.1177/0957650918812510
Fentaye Amare, D., Baheta Aklilu, T., & Gilani Syed Ihtsham, U.-H. (2018). Gas turbine gas-path fault identification using nested artificial neural networks. Aircraft Engineering and Aerospace Technology, 90(6), 992-999. doi:10.1108/AEAT-01-2018-0013
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Ganguli, R. (2003). Application of fuzzy logic for fault isolation of jet engines. Journal of Engineering for Gas Turbines and Power, 125(3), 617-623. doi:https://doi.org/10.1115/1.1470481
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Boyce, M. P., & Gonzalez, F. (2005). A Study of On-Line and Off-Line Turbine Washing to Optimize the Operation of a Gas Turbine. Journal of Engineering for Gas Turbines and Power, 129(1), 114-122. doi:10.1115/1.2181180
Dewallef, P., Romessis, C., Léonard, O., & Mathioudakis, K. (2004). Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics. Journal of Engineering for Gas Turbines and Power, 128(2), 281-287. doi:10.1115/1.2056507
Fentaye, A. D., Baheta, A. T., Gilani, S. I., & Kyprianidis, K. G. (2019). A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities. Aerospace, 6(7), 83. doi:https://doi.org/10.3390/aerospace6070083
Fentaye, A. D., & Kyprianidis, K. G. (2020). An intelligent data filtering and fault detection method for gas turbine engines. ICSC & ISATECH, 9 - 11 Oct., 2019, Amsterdam, the Netherlands. doi: https://doi.org/10.1051/matecconf/202031402007
Fentaye, A. D., Ul-Haq Gilani, S. I., Baheta, A. T., & Li, Y.-G. (2018). Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 233(6), 786-802. doi:10.1177/0957650918812510
Fentaye Amare, D., Baheta Aklilu, T., & Gilani Syed Ihtsham, U.-H. (2018). Gas turbine gas-path fault identification using nested artificial neural networks. Aircraft Engineering and Aerospace Technology, 90(6), 992-999. doi:10.1108/AEAT-01-2018-0013
Ganguli, R. (2002). Noise and outlier removal from jet engine health signals using weighted FIR median hybrid filters. Mechanical Systems and Signal Processing, 16(6), 967-978. https://doi.org/10.1006/mssp.2002.1477
Ganguli, R. (2003). Application of fuzzy logic for fault isolation of jet engines. Journal of Engineering for Gas Turbines and Power, 125(3), 617-623. doi:https://doi.org/10.1115/1.1470481
Hanachi, H., Liu, J., Banerjee, A., & Chen, Y. (2015). A framework with nonlinear system model and nonparametric noise for gas turbine degradation state estimation. Measurement Science and Technology, 26(6), 065604. doi:10.1088/0957-0233/26/6/065604
Hanachi, H., Liu, J., Kim, I. Y., & Mechefske, C. K. (2019). Hybrid sequential fault estimation for multi-mode diagnosis of gas turbine engines. Mechanical systems and signal processing, 115, 255-268. doi:https://doi.org/10.1016/j.ymssp.2018.05.054
Jaw, L. C., & Lee, Y.-J. (2014). Engine diagnostics in the eyes of machine learning. Proceedings of the 2014 ASME Turbo Expo, June 16-20, 2014, Düsseldorf, Germany. doi: https://doi.org/10.1115/GT2014-27088
Jiang, X., & Foster, C. (2013). Remote Thermal Performance Monitoring and Diagnostics: Turning Data Into Knowledge. ASME 2013 Power Conference, July 29–August 1, 2013, Boston, Massachusetts, USA. doi:10.1115/POWER2013-98246
Koskoletos, A. O., Aretakis, N., Alexiou, A., Romesis, C., & Mathioudakis, K. (2018). Evaluation of Aircraft Engine Gas Path Diagnostic Methods Through ProDiMES. Journal of Engineering for Gas Turbines and Power, 140(12). doi: https://doi.org/10.1115/1.4040909
Kramer, M. A. (1991). Nonlinear principal component analysis using autoassociative neural networks. AIChE journal, 37(2), 233-243. doi: https://doi.org/10.1002/aic.690370209
Kyprianidis, K. (2017). An Approach to Multi-Disciplinary Aero Engine Conceptual Design. International Symposium on Air Breathing Engines, ISABE 2017, Manchester, United Kingdom, 3-8 September 2017 Paper No. ISABE-2017-22661.
Kyprianidis, K. (2019). On Gas Turbine Conceptual Design. Ph.D. thesis, Cranfield University, UK.
Lambiris, B., Mathioudakis, K., Stamatis, A., & Papailiou, K. (1994). Adaptive modeling of jet engine performance with application to condition monitoring. Journal of propulsion and power, 10(6), 890-896. doi:https://doi.org/10.2514/3.23828
Lee, Y. K., Mavris, D. N., Volovoi, V. V., Yuan, M., & Fisher, T. (2010). A fault diagnosis method for industrial gas turbines using Bayesian data analysis. Journal of Engineering for Gas Turbines and Power, 132(4), 041602. doi:https://doi.org/10.1115/1.3204508
Li, Y.-G. (2017). Diagnostics of power setting sensor fault of gas turbine engines using genetic algorithm. The Aeronautical Journal, 121(1242), 1109-1130. doi:
Li, Y. (2010). Gas turbine performance and health status estimation using adaptive gas path analysis. Journal of Engineering for Gas Turbines and Power, 132(4), 041701. doi: https://doi.org/10.1115/1.3159378
Litt, J. S., Parker, K. I., & Chatterjee, S. (2003). Adaptive gas turbine engine control for deterioration compensation due to aging. Technical Report TM 2003-212607, NASA Glenn, Lewis, Ohio.
Loboda, I., & Olivares Robles, M. A. (2015). Gas turbine fault diagnosis using probabilistic neural networks. International Journal of Turbo & Jet-Engines, 32(2), 175-191. doi: 10.1515/tjj-2014-0019
Loboda, I., Pérez-Ruiz, J. L., & Yepifanov, S. (2018). A benchmarking analysis of a data-driven gas turbine diagnostic approach. Proceedings of ASME Turbo Expo 2018, Turbomachinery Technical Conference and Exposition, June 11-15, 2018, Oslo, Norway. doi: https://doi.org/10.1115/GT2018-76887
Losi, E., Venturini, M., Manservigi, L., Ceschini, G. F., & Bechini, G. (2019). Anomaly Detection in Gas Turbine Time Series by Means of Bayesian Hierarchical Models. Journal of Engineering for Gas Turbines and Power, 141(11).
Lu, F., Gao, T., Huang, J., & Qiu, X. (2019). A novel distributed extended Kalman filter for aircraft engine gas-path health estimation with sensor fusion uncertainty. Aerospace Science and Technology, 84, 90-106. doi:https://doi.org/10.1016/j.ast.2018.10.019
Marinai, L., Probert, D., & Singh, R. (2004). Prospects for aero gas-turbine diagnostics: a review. Applied Energy, 79(1), 109-126. doi:http://dx.doi.org/10.1016/j.apenergy.2003.10.005
Marinai, L., Singh, R., Curnock, B., & Probert, D. (2003). Detection and prediction of the performance deterioration of a turbofan engine. Proceedings of the International Gas Turbine Congress, November 2-7, 2003, Tokyo, Japan.
Mathioudakis, K., & Kamboukos, P. (2004). Assessment of the Effectiveness of Gas Path Diagnostic Schemes. Journal of Engineering for Gas Turbines and Power, 128(1), 57-63. doi:10.1115/1.1924535
Mathioudakis, K., & Romessis, C. (2016). Probabilistic neural networks for validation of on-board jet engine data. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 218(1), 59-72. doi:10.1177/095441000421800105
Mohammadi, E., & Montazeri-Gh, M. (2015). A fuzzy-based gas turbine fault detection and identification system for full and part-load performance deterioration. Aerospace Science and Technology, 46, 82-93. doi:http://dx.doi.org/10.1016/j.ast.2015.07.002
Ogaji, S. O. T., Sampath, S., Singh, R., & Probert, S. D. (2002). Parameter selection for diagnosing a gas-turbine's performance-deterioration. Applied Energy, 73(1), 25-46. doi:https://doi.org/10.1016/S0306-2619(02)00042-9
Pourbabaee, B., Meskin, N., & Khorasani, K. (2015). Sensor Fault Detection, Isolation, and Identification Using Multiple-Model-Based Hybrid Kalman Filter for Gas Turbine Engines. in IEEE Transactions on Control Systems Technology, vol. 24, no. 4, pp. 1184-1200, July 2016. doi:10.1109/TCST.2015.2480003.
Roumeliotis, I., Aretakis, N., Mathioudakis, K., & Yfantis, E. A. (2012). Modelling and Assessment of Compressor Faults on Marine Gas Turbines. ASME Turbo Expo 2012: Turbine Technical Conference and Exposition, June 11–15, 2012, Copenhagen, Denmark. doi:https://doi.org/10.1115/GT2012-69740
Sadough Vanini, Z. N., Meskin, N., & Khorasani, K. (2014). Multiple-model sensor and components fault diagnosis in gas turbine engines using autoassociative neural networks. Journal of Engineering for Gas Turbines and Power, 136(9). doi:https://doi.org/10.1115/1.4027215
Sapna, S., Tamilarasi, A., & Kumar, M. P. (2012). Backpropagation learning algorithm based on Levenberg Marquardt Algorithm. Comp Sci Inform Technol (CS and IT), 2, 393-398. doi: 10.5121/csit.2012.2438
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. International conference on prognostics and health management. 6-9 Oct. 2008, Denver, CO, USA. doi:10.1109/PHM.2008.4711414
Simon, D. L. (2010a). An integrated architecture for on-board aircraft engine performance trend monitoring and gas path fault diagnostics. Proceedings of the 2010 JANNAF Joint Subcommittee Meeting, Colorado Springs, CO, May 3–7. doi:10.2514/6.2014-3924
Simon, D. L. (2010b). Propulsion diagnostic method evaluation strategy (ProDiMES) user's guide. NASA/TM—2010-215840. doi: 20100005639
Simon, D. L., Borguet, S., Léonard, O., & Zhang, X. F. (2014). Aircraft engine gas path diagnostic methods: public benchmarking results. Journal of Engineering for Gas Turbines and Power, 136(4), 041201.doi: https://doi.org/10.1115/1.4025482
Stenfelt, M., Zaccaria, V., & Kyprianidis, K. G. (2019). Automatic gas turbine matching scheme adaptation for robust GPA diagnostics. Proceedings of the ASME Turbo Expo 2019: Power for Land, Sea and Air, June 11-15, 2019, Phoenix, Arizona, USA. GT2019-91018. doi:https://doi.org/10.1115/GT2019-91018
Tahan, M., Tsoutsanis, E., Muhammad, M., & Karim, Z. A. (2017). Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review. Applied Energy, 198, 122-144. doi: https://doi.org/10.1016/j.apenergy.2017.04.048
Tang, S., Tang, H., & Chen, M. (2019). Transfer-learning based gas path analysis method for gas turbines. Applied Thermal Engineering, 155, 1-13. doi: https://doi.org/10.1016/j.applthermaleng.2019.03.156
Uday, P., & Ganguli, R. (2010). Jet engine health signal denoising using optimally weighted recursive median filters. Journal of Engineering for Gas Turbines and Power, 132(4). doi: https://doi.org/10.1115/1.3200907
Urban, L. A. (1969). Gas turbine engine parameter interrelationships: 2d ed. Hamilton Standard Division of United Aircraft Windsor Locks, USA.
Van der A., H., Anthonis, J., De Bruyne, S., & Leuridan, J. J. E. w. C. (2013). Virtual engineering at work: the challenges for designing mechatronic products. 29(3), 389-408.doi:10.1007/s00366-012-0286-6
Wei, Z., Zhang, S., Jafari, S., & Nikolaidis, T. (2020). Gas turbine aero-engines real time on-board modelling: A review, research challenges, and exploring the future. Progress in Aerospace Sciences, 121, 100693. doi: https://doi.org/10.1016/j.paerosci.2020.100693
Verma, R., & Ganguli, R. (2005). Denoising jet engine gas path measurements using nonlinear filters. IEEE/ASME Transactions on Mechatronics, 10(4), 461-464. doi: 10.1109/TMECH.2005.852454
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Technical Papers