Open Access
Issue
MATEC Web Conf.
Volume 314, 2020
International Cross-Industry Safety Conference (ICSC) – International Symposium on Aircraft Technology, MRO and Operations (ISATECH) (ICSC-ISATECH 2019)
Article Number 02007
Number of page(s) 15
Section International Symposium on Aircraft Technology, MRO and Operations
DOI https://doi.org/10.1051/matecconf/202031402007
Published online 29 May 2020
  1. A. D. Fentaye, A. T. Baheta, S. I. Gilani, and K. G. Kyprianidis, “A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities”, Aerospace, vol. 6, p. 83, (2019). [CrossRef] [Google Scholar]
  2. Y. Qingcai, S. Li, Y. Cao, and N. Zhao, “Full and Part-Load Performance Deterioration Analysis of Industrial Three-Shaft Gas Turbine Based on Genetic Algorithm”, In Proceedings of the ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition, Harbin, China, (13–17 June 2016); p. V006T005A016. [Google Scholar]
  3. T. Kobayashi and D. L. Simon, “Evaluation of an enhanced bank of Kalman filters for in-flight aircraft engine sensor fault diagnostics”, J ENG GAS TURB POWER, vol. 127, pp. 497-504, (2005). [CrossRef] [Google Scholar]
  4. A. J. Volponi, H. DePold, R. Ganguli, and C. Daguang, “The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study”, J ENG GAS TURB POWER, vol. 125, pp. 917-924, (2003). [CrossRef] [Google Scholar]
  5. B. Pourbabaee, N. Meskin, and K. Khorasani, “Sensor Fault Detection, Isolation, and Identification Using Multiple-Model-Based Hybrid Kalman Filter for Gas Turbine Engines”, IEEE Transactions on Control Systems Technology, (2015). [Google Scholar]
  6. R. Ganguli, Gas Turbine Diagnostics: Signal Processing and Fault Isolation: CRC press: Boca Raton, FL, USA, (2012). [CrossRef] [Google Scholar]
  7. F. Lu, Y. Chen, J. Huang, D. Zhang, and N. Liu, “An integrated nonlinear model-based approach to gas turbine engine sensor fault diagnostics”, Proc IMechE Part G: J Aerospace Engineering, vol. 228, pp. 2007-2021, (2014). [CrossRef] [Google Scholar]
  8. F. Lu, T. Gao, J. Huang, and X. Qiu, “A novel distributed extended Kalman filter for aircraft engine gas-path health estimation with sensor fusion uncertainty”, Aerospace Science and Technology, vol. 84, pp. 90-106, (2019). [CrossRef] [Google Scholar]
  9. Q. Yang, S. Li, and Y. Cao, “Multiple model-based detection and estimation scheme for gas turbine sensor and gas path fault simultaneous diagnosis”, JMST, vol. 33, pp. 1959-1972, (2019). [Google Scholar]
  10. V. Zaccaria, M. Stenfelt, I. Aslanidou, and K. G. Kyprianidis, “Fleet Monitoring and Diagnostics Framework Based on Digital Twin of Aero-Engines”, in ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition, Oslo, Norway (June 1115, 2018). [Google Scholar]
  11. M. Rahman, V. Zaccaria, X. Zhao, and K. Kyprianidis, “Diagnostics-Oriented Modelling of Micro Gas Turbines for Fleet Monitoring and Maintenance Optimization”, Processes, vol. 6, p. 216, (2018). [CrossRef] [Google Scholar]
  12. D. Zeng, D. Zhou, C. Tan, and B. Jiang, “Research on Model-Based Fault Diagnosis for a Gas Turbine Based on Transient Performance”, Applied Sciences, vol. 8, p. 148, (2018). [CrossRef] [Google Scholar]
  13. M. A. Kramer, “Nonlinear Principal Component Analysis Using Autoassociative Neural Networks”, AIChE Journal, vol. 37, p. 233, (1991). [CrossRef] [Google Scholar]
  14. M. A. Kramer, “Neutral network applications in chemical engineeringAutoassociative neural networks”, COMPUT CHEM ENG, vol. 16, pp. 313-328, 1992/04/01 (1992). [CrossRef] [Google Scholar]
  15. P. J. Lu and T. C. Hsu, “Application of autoassociative neural network on gas-path sensor data validation”, J PROPUL POWER, vol. 18, pp. 879-888, (2002). [CrossRef] [Google Scholar]
  16. R. Ganguli and B. Dan, “Trend Shift Detection in Jet Engine Gas Path Measurements Using Cascaded Recursive Median Filter With Gradient and Laplacian Edge Detector”, J ENG GAS TURB POWER, vol. 126, pp. 55-61, (2004). [CrossRef] [Google Scholar]
  17. S. Sina Tayarani-Bathaie and K. Khorasani, “Fault detection and isolation of gas turbine engines using a bank of neural networks”, J PROCESS CONTR, vol. 36, pp. 22-41, 12// (2015). [CrossRef] [Google Scholar]
  18. M. Amozegar and K. Khorasani, “An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines”, Neural Networks, vol. 76, pp. 106-121, 4// (2016). [CrossRef] [Google Scholar]
  19. Z. N. Sadough Vanini, N. Meskin, and K. Khorasani, “Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks”, J ENG GAS TURB POWER, vol. 136, p. 091603, (2014). [CrossRef] [Google Scholar]
  20. I. Loboda and M. A. Olivares Robles, “Gas turbine fault diagnosis using probabilistic neural networks”, INT J TURBO JET ENG., vol. 32, pp. 175-191, (2015). [Google Scholar]
  21. A. D. Fentaye, A. T. Baheta, and S. I. U.-H. Gilani, “Gas turbine gas-path fault identification using nested artificial neural networks”, Aircraft Engineering and Aerospace Technology, vol. 90, pp. 992-999, (2018). [CrossRef] [Google Scholar]
  22. D. Amare, T. Aklilu, and S. Gilani, “Gas path fault diagnostics using a hybrid intelligent method for industrial gas turbine engines”, J. Braz. Soc. Mech. Sci. & Eng, vol. 40, p. 578, (2018). [CrossRef] [Google Scholar]
  23. W. Donat, K. Choi, W. An, S. Singh, and K. Pattipati, “Data visualization, data reduction and classifier fusion for intelligent fault diagnosis in gas turbine engines”, J ENG GAS TURB POWER, vol. 130, p. 041602, (2008). [CrossRef] [Google Scholar]
  24. I. Loboda, “Gas turbine fault classification using probability density estimation”, in Proceedings of ASME Turbo Expo: Turbine Technical Conference and Exposition, GT2014-27265, Germany, Dusseldorf, (2014). [Google Scholar]
  25. A. Fentaye, V. Zaccaria, M. Rahman, M. Stenfelt, and K. Kyprianidis, “Hybrid modelbased and data-driven diagnostic algorithm for gas turbine engines”, Proceedings of ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition, GT2020-14481, (2020). [Google Scholar]
  26. A. D. Fentaye, S. I. Ul-Haq Gilani, A. T. Baheta, and Y.-G. Li, “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, vol. 233, pp. 786-802, (2019). [CrossRef] [Google Scholar]
  27. P. J. Lu, M. C. Zhang, T. C. Hsu, and J. Zhang, “An evaluation of engine faults diagnostics using artificial neural networks”, J ENG GAS TURB POWER, vol. 123, pp. 340-346, (2001). [CrossRef] [Google Scholar]
  28. R. Bettocchi, M. Pinelli, P. R. Spina, and M. Venturini, “Artificial Intelligence for the Diagnostics of Gas Turbines—Part I: Neural Network Approach”, J ENG GAS TURB POWER, vol. 129, pp. 711-719, (2006). [CrossRef] [Google Scholar]
  29. S. Borra and A. D. Ciaccio, “Measuring the prediction error. A comparison of cross validation, bootstrap and covariance penalty methods”, Computational Statistics & Data Analysis, vol. 54, pp. 2976-2989, (2010). [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.