Journal of Engineering Sciences

Author(s): Verbovyi A.^1*, Neamtu C.², Sieryk, M.¹, Vashyst B.¹, Pavlenko V.³, Simonovskiy V.¹, Pavlenko, I.¹

Affiliation(s): 
¹ Sumy State University, 2 Rymskogo-Korsakova St., 40007 Sumy, Ukraine;
² Technical University of Cluj-Napoca, 28 Memorandumului St., 400114 Cluj-Napoca, Romania;
³ Machine Building College of Sumy State University, 17 T. Shevchenka Ave., 40000 Sumy, Ukraine

^*Corresponding Author’s Address: [email protected]

Issue: Volume 6; Issue 2 (2019)

Dates:
Paper received: August 17, 2019
The final version of the paper received: December 5, 2019
Paper accepted online: December 10, 2019

Citation:
Verbovyi, A., Neamtu, C., Sieryk, M., Vashyst, B., Pavlenko, V., Simonovskiy, V., Pavlenko, I. (2019). Ensuring the vibration reliability of rotors connected by spline joints. Journal of Engineering Sciences, Vol. 6(2), pp. D14-D19, doi: 10.21272/jes.2019.6(2).d3

DOI: 10.21272/jes.2019.6(2).d3

Research Area:  MECHANICAL ENGINEERING: Dynamics and Strength of Machines

Abstract. This article is devoted to the development of refined numerical mathematical models of rotor dynamics of high-performance turbomachines having a spline connection. These models consider the dependence of the critical frequencies of the shaft on the angular stiffness of the spline connection, as well as the procedure of virtual balancing. As a result of the complex application of this approach, the methods of calculation of vibration characteristics taking into ac-count variable values of angular rigidity of splined connection are offered. In addition, the method of evaluating the system of initial imbalances with the corresponding displacements of the rotor axis in the correction and calculation sections has also been improved. The proposed approaches, based on the integrated application of CAE software and computational intelligent systems, allow for modal and harmonic analysis and implement virtual balancing with a significant reduction in preparation and machine time without loss of relative accuracy. In addition, the developed mathematical model of free and forced vibrations of rotor systems have been implemented in the program code operational files “Critical Frequencies of the Rotor” and “Forced Oscillations of the Rotor” of the computer algebra system MathCAD that allows improving the dynamic balancing procedure for evaluating primary imbalances. The high accuracy of the proposed approach is confirmed by checking the dynamic deviations of the rotor axis by the system of residual imbalances in accordance with the standards of vibration stability.

Keywords: turbomachine, spline connection, angular stiffness, virtual balancing, modal analysis, harmonic analysis.

References:

1. Pavlenko, I. V., Simonovskiy, V. I., Pitel’, J., Demianenko, M. (2018). Dynamic Analysis of Centrifugal Machines Rotors with Combined Using 3D and 2D Finite Element Models. Sumy State University, Sumy, Ukraine.
2. Pavlenko, I. V., Simonovskiy, V. I., Demianenko, M. M. (2017). Dynamic analysis of centrifugal machines rotors supported on ball bearings by combined application of 3D and beam finite element models. IOP Conference Series: Materials Science and Engineering, Vol. 233(1), article number 012053, doi: 10.1088/1757-899X/233/1/012053.
3. Yashchenko, A. S., Rudenko, A. A., Simonovskiy, V. I., Kozlov, O. M. (2017). Effect of bearing housings on centrifugal pump rotor dynamics. IOP Conference Series: Materials Science and Engineering, Vol. 233(1), article number 012054, doi: 10.1088/1757-899X/233/1/012054.
4. Pavlenko, I., Ivanov, V., Kuric, I., Gusak, O., Liaposhchenko, O. (2019). Ensuring vibration reliability of turbopump units using artificial neural networks. Advances in Manufacturing II – Volume 1. Lecture Notes in Mechanical Engineering. Springer, Cham, pp. 165–175, 2019, doi: 10.1007/978-3-030-18715-6_14.
5. Pavlenko, I., Simonovskiy, V., Ivanov, V., Zajac, J., Pitel, J. (2019). Application of artificial neural network for identification of bearing stiffness characteristics in rotor dynamics analysis. Advances in Design, Simulation and Manufacturing, DSMIE 2018, Lecture Notes in Mechanical Engineering, Springer, pp. 325–335, doi: 10.1007/978-3-319-93587-4_34.
6. Ding, F., Wang, Z., Qin, F. (2015). Two kinds of neural network fusion of aero-engine rotor vibration signal fault diagnosis. 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering, pp. 1546–1552.
7. Tanoh, A., Konan, D. K., Koffi, M., Yeo, Z., Kouacou, M. A., Koffi, B. K., N’guessan, K. R. (2008). A neural network application for diagnosis of the asynchronous machine. Journal of Applied Sciences, Vol. 8, pp. 3528–3531, doi: 10.3923/jas.2008.3528.3531.
8. Pavlenko, I., Neamtu, C., Verbovyi, A., Pitel, J., Ivanov, V., Pop, G. (2019). Using computer modeling and artificial neural networks for ensuring the vibration reliability of rotors. CEUR Workshop Proceedings, Vol. 2353, pp. 702–716.
9. Pavlenko, I., Trojanowska, J., Gusak, O., Ivanov, V., Pitel, J., Pavlenko, V. (2019). Estimation of the reliability of automatic axial-balancing devices for multistage centrifugal pumps. Periodica Polytechnica Mechanical Engineering, Vol. 63(1), pp. 277–281, doi: 10.3311/PPme.12801.
10. Kim, Y. W., Jeong, W. B. (2018). Reliability evaluation technique of compressor using pressure pulsation and vibration signals. Journal of Physics: Conference Series, Vol. 1075, article number 012076, doi: 10.1088/1742-6596/1075/1/012076.
11. Ben Rahmoune, M., Hafaifa, A., Guemana, M. (2015). Neural network monitoring system used for the frequency vibration prediction in gas turbine. 3rd International Conference on Control, Engineering and Information Technology, article number 15418537, doi: 10.1109/CEIT.2015.7233185.
12. Pavlenko, I., Trojanowska, J., Ivanov, V., Liaposhchenko, O.: Scientific and methodological approach for the identification of mathematical models of mechanical systems by using artificial neural networks. 3rd Conference on Innovation, Engineering and Entrepreneurship, Regional HELIX 2018, Lecture Notes in Electrical Engineering, Springer, Vol. 505, pp. 299–306, doi: 10.1007/978-3-319-91334-6_41.
13. Manjurul, M. M., Kim, I.-M. (2018). Motor bearing fault diagnosis using deep convolutional neural networks with 2D analysis of vibration signal. Lecture Notes in Computer Science, Vol. 10832, pp. 144–155, doi: 10.1007/978-3-319-89656-4_12.

Full Text