Periodically Non-Stationary Properties of Vibrations in a Gas Turbine Engine with an Unbalanced Rotor

I. JAVORSKYJ; Yu. TORBA; R. YUZEFOVYCH; Ye. SBRODOV; O. LYCHAK

doi:10.15407/scine21.06.038

Authors

I. JAVORSKYJ Karpenko Physico-Mechanical Institute National Academy of Sciences of Ukraine https://orcid.org/0000-0003-0243-6652
Yu. TORBA Joint Stock Company Zaporizhzhia Machine-Building Design Bureau PROGRESS named after Academician O. H. Ivchenko https://orcid.org/0000-0001-8470-9049
R. YUZEFOVYCH Karpenko Physico-Mechanical Institute National Academy of Sciences of Ukraine https://orcid.org/0000-0001-5546-453X
Ye. SBRODOV Joint Stock Company Zaporizhzhia Machine-Building Design Bureau PROGRESS named after Academician O. H. Ivchenko https://orcid.org/0009-0005-7614-008X
O. LYCHAK Karpenko Physico-Mechanical Institute National Academy of Sciences of Ukraine https://orcid.org/0000-0001-5559-1969

DOI:

https://doi.org/10.15407/scine21.06.038

Keywords:

gas-turbine engine, vibration, periodical non-stationary random processes, frequency estimator, mean function, variance

Abstract

Introduction. Transform of the vibration signal, while the power spectrum has been estimated through the Blackman—Tukey method. However, because experimental vibration data are characterized by a combination of harmonic and stochastic processes, both techniques have proven inadequate for analyzing mixed signals.
Problem Statement. The evaluation of rotor balance in gas turbine engines has been commonly based on the
magnitude of the regular component of vibrations at the rotor speed. The amplitude spectrum of a gas turbine engine has traditionally been determined using the Fourier mponent of vibrations at the rotor’s rotational frequency. Yet, the vibration signal contains numerous additional components, including stochastic ones, which should be accounted for to obtain reliable estimates. Modeling the vibration signal as a periodically non-stationary random process (PNRP) has allowed separating the deterministic and the stochastic components.

Purpose. The study has aimed to conduct a comparative analysis of periodic non-stationarity in vibration signals of gas turbine engines with balanced and unbalanced rotors.
Materials and Methods. Vertical vibration components of balanced and unbalanced gas turbine engines in the low-frequency range (<2 kHz) have been analyzed. The vibration signal has been modeled as a periodically non-stationary random process, and an LS-functional has been applied to identify the fundamental frequencies of both deterministic and stochastic components.
Results. Correlation and spectral functions of vibration signals have been estimated, and the fundamental frequencies of their regular and stochastic components have been determined. It has been shown that the vibration spectra of engines with both balanced and unbalanced rotors are mixed. The deterministic component has exhibited a polyharmonic spectrum, and the harmonic amplitudes have been quantified. Furthermore, engine vibrations have been shown to exhibit second-order periodic nonstationarity.
Conclusions. The proposed vibration indicators have provided a basis for improving engine balancing during adjustment and repair procedures. The study has also highlighted the potential of these indicators for early detection of rotor defects. Future research will focus on analyzing the polyharmonic structure of vibration spectra and developing a refi ned methodology for defect
diagnostics at incipient stages.

Downloads

Download data is not yet available.

References

Djaidir, B., Hafaifa, A., Kouzou, A. (2017). Faults detection in gas turbine rotor using vibration analysis under varying conditions. J. theoretical and applied mechanics, 55(2), 393—406. https://doi.org/10.15632/jtam-pl.55.2.393

Fábry, S., Češkovič, M. (2017). Aircraft gas turbine engine vibration diagnostics. Review Articles, 5(4), 24—28. https:// doi.org/10.14311/MAD.2017.04.04

Pérez-Ruiz, J. L., Luis, J., Tang, Yu., Loboda, I., Miró-Zárate, L. A. (2024). An Integrated Monitoring, Diagnostics, and Prognostics System for Aero-Engines under Long-Term Performance Deterioration. Aerospace, 11(3), 217. https://doi. org/10.3390/aerospace11030217.

Rath, N., Mishra, R., Kushari, A. (2022). Aero engine health monitoring, diagnostics and prognostics for condition-based maintenance: an overview. Int. J. Turbo Jet. Engines. 40(s1), s279—s292. https://doi.org/10.1515/tjeng-2022-0020

Grządziela, A. (2006). Analysis of vibration parameters of ship gas turbine engines. Polish Maritime Research, 13(2), 22—26. URL: https://journal.mostwiedzy.pl/pmr/article/view/1230/1156 (Last accessed: 07.07.2025).

Fentaye, A. D., Baheta, A. T., Gilani, S. I., Kyprianidis, K. G. (2019). A review on gas turbine gas-path diagnostics: stateof-the-art methods, challenges and opportunities. Aerospace, 6(7), 83. https://doi.org/10.3390/aerospace6070083

Mishra, R. K., Thomas, J., Srinivasan, K., Nandi, V., Bhatt, R. R. (2015). Investigation of HP turbine blade failure in a military turbofan engine. Int. J. Turbo Jet Engines, 34(1), 23—31. https://doi.org/10.1515/tjj-2015-0049

Hanachi, H., Mechefske, C., Liu, J., Banerjee, A., Chen, Y. (2018). Performance-based gas turbine health monitoring, diagnostics, and prognostics: a survey. IEEE Trans. Reliab., 67(3), 1340—1363. https://doi.org/10.1109/tr.2018. 2822702

Tahan, M., Tsoutsanis, E., Muhammad, M., Karim, A. (2017). Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review. Appl. Energy, 198, 122—144. https://doi.org/10. 1016/j.apenergy.2017.04.048

Zaidan, M., Relan, R., Mills, A., Harrison, R. (2015). Prognostics of gas turbine engine: An integrated approach. Expert Systems with Applications, 42(22), 8472—8483. https://doi.org/10.1016/j.eswa.2015.07.003

Stamatis, A. G. (2011). Evaluation of gas path analysis methods for gas turbine diagnosis. J. Mech. Sci. Technol., 25(2), 469—477. https://doi.org/10.1007/s12206-010-1207-5

Heng, A., Zhang, S., Tan, A., Mathew, J. (2009) Rotating machinery prognostics: state of the art, challenges and opportunities. Mech. Syst. Signal Process., 23(3), 724—739. https://doi.org/10.1016/j.ymssp.2008.06.009

Muszynska, A. (1995). Vibrational Diagnostics of Rotating Machinery Malfunctions. Int. J. Rotating Machinery, 1(3—4), 237—266. https://doi.org/10.1155/S1023621X95000108

Jardine, A., Lin, D., Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing conditionbased maintenance. Mech. Syst. Signal Process., 20(7), 1483—1510. https://doi.org/10.1016/j.ymssp.2005.09.012

Jiang, X., Yang, S., Wang, F., Xu, S., Wang, X., Cheng, X. (2021). OrbitNet: A new CNN model for automatic fault diagnostics of turbomachines. Applied Soft Computing, 110, 107702. https://doi.org/10.1016/j.asoc.2021.107702

Wang, C., Chen, C. (2001). Bifurcation of Self-Acting Gas Journal Bearings. ASME J. Tribol., 123(4), 755—767. https:// doi.org/10.1115/1.1388302

Aretakis, N., Mathioudakis, K., Kefalakis, M., Papailiou, K. (2004). Turbocharger Unstable Operation Diagnosis Using Vibroacoustic Measurements. ASME. J. Eng. Gas Turbines Power, 126(4), 840—847. https://doi.org/10.1115/1.1771686

Bonello, P., Pham, H. M. (2014). Nonlinear Dynamic Analysis of High Speed Oil-Free Turbomachinery With Focus on Stability and Self-Excited Vibration. ASME. J. Tribol., 136(4), 041705. https://doi.org/10.1115/1.4027859

Soleimani, M., Campean, F., Neagu, D. (2021). Diagnostics and prognostics for complex systems: a review of methods and challenges. Qual. Reliab. Eng. Int., 37(7), 3746—3778. https://doi.org/10.1002/qre.2947

Lakshminarasimha, A. N., Boyce, M. P., Meher-Homji, C. B. (1994). Modeling and analysis of gas turbine performance deterioration. J. Eng. Gas Turbines Power, 116(1), 46—52. https://doi.org/10.1115/1.2906808

Marinai, L., Probert, D., Singh, R. (2004). Prospects for aero gas-turbine diagnostics: a review. Appl. Energy, 79(1), 109— 126. https://doi.org/10.1016/j.apenergy.2003.10.005

Napolitano, A. (2020). Cyclostationary processes and time series: Theory, applications, and generalizations. Elsevier, Academic Press, 2020. https://doi.org/10.1016/C2017-0-04240-4

Koopmans, L. H. (1974). The spectral analysis of time series [by] L. H. Koopmans. New York.

Kay, S. M. (1988). Modern Spectral Estimation: Theory and Application. New Jersey.

Javorskyj, I. (2013). Mathematical models and analysis of stochastic oscillations. Lviv [in Ukrainian].

Javorskyj, I., Matsko, I., Yuzefovych, R., Lychak, O., Lys, R. (2021). Methods of hidden periodicity discovering for gearbox fault detection. Sensors, 21(18), 6138. https://doi.org/10.3390/s21186138

Javorskyj, I., Yuzefovych, R., Matsko, I., Zakrzewski, Z., Majewski, J. (2017). Coherent covariance analysis of periodically correlated random processes for unknown non-stationarity period. Dig. Signal Process., 65, 27—51. https://doi.org/10. 1016/j.dsp.2017.02.013

Javorskyj, I., Yuzefovych, R., Matsko, I., Zakrzewski, Z. (2022). The least square estimation of the basic frequency for periodically non-stationary random signals. Dig. Sign. Proc., 122, 103333. https://doi.org/10.1016/j.dsp.2021. 103333

Javorskyj, I., Yuzefovych, R., Lychak, O., Semenov, P., Sliepko, R. (2023). Detection of distributed and localized faults in rotating machines using periodically non-stationary covariance analysis of vibrations. Measurement Science and Technology, 34(6), 065102. https://doi.org/10.1088/1361-6501/acbc93