A case study of vibration fault diagnosis applied at Rolls-Royce T-56 turboprop engine

    Christos Skliros   Affiliation


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 (Dec. 2019), 78-82. DOI:
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Dec 31, 2019
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This work is licensed under a Creative Commons Attribution 4.0 International License.


Cubillo, A., Perinpanayagam, S. & Esperon-Miguez, M. (2016). A review of physics-based models in prognostics: application to gears and bearings of rotating machinery. Advances in Mechanical Engineering, 8(8), 1–21.

Djaidir, B., Hafaifa, A., & Kouzou, A. (2017). Faults detection in gas turbine rotor using vibration analysis under varying conditions. Journal of Theoretical and Applied Mechanics, 2014, 393.

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.

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.

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.

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.

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.

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.

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.

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.

Kleer, J. De, & Kurien, J. (2003). Fundamentals of model-based diagnosis. IFAC Proceedings Volumes (IFAC-Papers Online), 6670 (June 2003), 25.

Milne, D., Pen L., Thompson D. & Powrie W. (2018). Automated processing of railway track deflection signals obtained from velocity and acceleration measurements. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 232(8), 2097–2110.

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.

Mobley, R. K. (2002). An introduction to predictive maintenance. Elsevier.

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).

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.

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.

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.

Vachtsevanos, G., Lewis, F., Roemer, M. J., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems. John Wiley & Sons, Inc.

Walsh, P. P., & Fletcher, P. (2004). Gas turbine performance. Blackwell Science Ltd.