A case study of vibration fault diagnosis applied at Rolls-Royce T-56 turboprop engine
Abstract
Gas turbine engines include a plethora of rotating modules, and each module consists of numerous components. A component’s mechanical fault can result in excessive engine vibrations. Identification of the root cause of a vibration fault is a significant challenge for both engine manufacturers and operators. This paper presents a case study of vibration fault detection and isolation applied at a Rolls-Royce T-56 turboprop engine. In this paper, the end-to-end fault diagnosis process from starting system faults to the isolation of the engine’s shaft that caused excessive vibrations is described. This work contributes to enhancing the understanding of turboprop engine behaviour under vibration conditions and highlights the merit of combing information from technical logs, maintenance manuals and engineering judgment in successful fault diagnosis.
First published online 22 January 2020
Keyword : gas turbines, vibration, diagnostics, condition-based maintenance, fault detection, fault isolation
How to Cite
Skliros, C. (2019). A case study of vibration fault diagnosis applied at Rolls-Royce T-56 turboprop engine. Aviation, 23(3), 78-82. https://doi.org/10.3846/aviation.2019.11900
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Durkin, J. (1994). Expert systems: design and development. New York: Macmillan Publishing Company.
Evan, C. P. (2012). Industrial internet: pushing the boundaries of minds and machines – general electric. Imagination at work. http://www.ge.com/docs/chapters/Industrial_Internet.pdf
Fedoronchak, T., & Kolpakova, T. (2018). Study on vibrations diagnostics of gas turbine engines with wavelets. In Modern Problems of Radio Engineering, Telecommunications and Computer Science, International Conference, 6, 4. Lviv-Slavske, Ukraine: IEEE. https://doi.org/10.1109/TCSET.2018.8336348
Gao, Z., Cunbao, M., Dong, S., & Yang, L. (2017). Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis. Neurocomputing, 238(C), 13–23. https://doi.org/10.1016/j.neucom.2017.01.032
Haidong, S., Jiang, H., Zhao, K., Wei, D., & Li, X. (2018). A novel tracking deep wavelet auto-encoder method for intelligent fault diagnosis of electric locomotive bearings. Mechanical Systems and Signal Processing, 110, 193–209. https://doi.org/10.1016/j.ymssp.2018.03.011
Hu, Q., Aisong, Q., Qinghua, Z., Jun, H., & Guoxi, S. (2018). Fault diagnosis based on weighted extreme learning machine with wavelet packet decomposition and KPCA. IEEE Sensors Journal, PP(c), 1–1. https://doi.org/10.1109/JSEN.2018.2866708
Hungate, D. (1979). Lockheed Martin: service news. Lockheed-Georgia Company.
Hwang, I., Sungwan, K., Youdan, K., Chze Eng, S. (2010). A survey of fault detection, isolation, and reconfiguration methods. IEEE Transactions on Control Systems Technology, 18(3), 636–653. https://doi.org/10.1109/TCST.2009.2026285
Jackson, P. (ed.). (1997). Jane’s all the world’s aircraft (87th ed.). Jane’s Information Group.
Jennions, I. K. (ed.). (2011). Integrated vehicle health management – perspectives on an emerging field. SAE International. https://doi.org/10.4271/R-405
Jia, F., Yaguo, L., Na, L., & Saibo, X. (2018). Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization. Mechanical Systems and Signal Processing, 110, 349–67. https://doi.org/10.1016/j.ymssp.2018.03.025
Kleer, J. De, & Kurien, J. (2003). Fundamentals of model-based diagnosis. IFAC Proceedings Volumes (IFAC-Papers Online), 6670 (June 2003), 25. https://doi.org/10.1016/S1474-6670(17)36467-4
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Mirsaitov, F., & Ignatkov, K. A. (2018). Gas turbine engine in-flight diagnostics using 3D vibration spectra. Proceedings – 2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2018, 275–278. https://doi.org/10.1109/USBEREIT.2018.8384603
Mobley, R. K. (2002). An introduction to predictive maintenance. Elsevier. https://doi.org/10.1016/B978-075067531-4/50006-3
Muhammad, M., Masdi, B., Sarwar, U., Tahan, M. R., & Karim, A. (2016). Fault diagnostic model for rotating machinery based on principal component analysis and neural network. ARPN Journal of Engineering and Applied Sciences, 11(24), 14327–14331.
Nivesrangsan, P. (2018). Bearing fault monitoring by comparison with main bearing frequency components using vibration signal. In 2018 5th International Conference on Business and Industrial Research (ICBIR) (pp. 292–296). https://doi.org/10.1109/ICBIR.2018.8391209
Saha, B., & Vachtsevanos, G. (2006). A model-based reasoning approach to system fault diagnosis. In Proceedings of the 10th WSEAS International Conference on Systems, 2006, 64–71. http://dl.acm.org/citation.cfm?id=1984211.1984224
Silva, A., Zarzo, A., Munoz-Guijosa, J. M., & Miniello, F. (2018). Evaluation of the continuous wavelet transform for detection of single-point rub in aeroderivative gas turbines with accelerometers. Sensors (Switzerland), 18(6), 1–22. https://doi.org/10.3390/s18061931
Teng, W., Ding, X., Zhang, X., Liu, Y., & Ma, Z. (2016). Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform. Renewable Energy, 93, 591–598. https://doi.org/10.1016/j.renene.2016.03.025
Vachtsevanos, G., Lewis, F., Roemer, M. J., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems. John Wiley & Sons, Inc. https://doi.org/10.1002/9780470117842
Walsh, P. P., & Fletcher, P. (2004). Gas turbine performance. Blackwell Science Ltd. https://doi.org/10.1002/9780470774533