Identification of rotor bearing parameters using vibration response data in a turbocharger rotor

Mutra, Rajasekhara Reddy; Srinivas, J.

doi:10.22061/jcarme.2018.3165.1347

تعداد نشریات	15
تعداد شماره‌ها	239
تعداد مقالات	2,430
تعداد مشاهده مقاله	4,047,882
تعداد دریافت فایل اصل مقاله	2,939,775

	Identification of rotor bearing parameters using vibration response data in a turbocharger rotor
Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقاله 11، دوره 9، شماره 1 - شماره پیاپی 17، مهر 2019، صفحه 145-156 اصل مقاله (1.37 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2018.3165.1347
نویسندگان
Rajasekhara Reddy Mutra^* ؛ J. Srinivas
Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha,769008, India
تاریخ دریافت: 21 آذر 1396، تاریخ بازنگری: 01 آبان 1397، تاریخ پذیرش: 05 آبان 1397
چکیده
Turbochargers are most widely used in automotive, marine and locomotive applications with diesel engines. To increase the engine performance nowadays, in aerospace applications also turbochargers are used. Mostly the turbocharger rotors are commonly supported over the fluid film bearings. With the operation, lubricant properties continuously alter leading to different load bearing capacities. This paper deals with the diagnostic approach for prediction of shaft unbalance and the bearing parameters using the measured frequency responses at the bearing locations. After validating the natural frequencies of the rotor finite element model with experimental analysis, the response histories of the rotor are recorded. The influence of the parameters such as bearing clearance, oil viscosity and casing stiffness on the unbalance response is studied. By considering three levels each for shaft unbalance and oil viscosity, the output data in terms of four statistical parameters of equivalent Hilbert envelopes in the frequency domain are measured. The data is inversely trained using Radial Basis Function (RBF) neural network model to predict the unbalance and oil viscosity indices from given output response characteristics. The outputs of the RBF model are validated thoroughly. This approach finds changes in the rotor bearing parameters from the measured responses in a dynamic manner. The results indicate that there is an appreciable effect of lubricant viscosity at two different temperatures compared to other parameters within the operating speed range. The identification methodology using the neural network is quite fast and reliable
کلیدواژه‌ها
Turbocharger؛ Diagnostic techniques؛ Hilbert transforms؛ RBF Network

مراجع
[1] N.F. Sakellaridis, S.I. Raptotasios, A.K. Antonopoulos, G.C. Mavropoulos, D.T. Hountalas, "Development and validation of a new turbocharger simulation methodology for marine two stroke diesel engine modelling and diagnostic applications", Energy. Vol. 91, No. 1, pp.952–966 (2015). [2] A.A. Kozhenkov, R.S. Deitch, 'Three-Dimensional Finite Element Simulation of Nonlinear Dynamic Rotor Systems of a Turbocharger", J. Vib. Acoust. Vol. 130, No. 3, pp. 031003-8, (2008). [3] Hao Zhang, Zhanqun Shi, Shunxin Zhang, Fengshou Gu, Andrew Ball, "Stability analysis for a turbocharger rotor system under nonlinear hydrodynamic forces", Research and Essay. Vol. 8, No. 1, pp.1495–1511, (2013). [4] L. Wang, G. Bin, X. Li, X. Zhang, "Effects of floating ring bearing manufacturing tolerance clearances on the dynamic characteristics for turbocharger", Chin. J. Mech. Eng, Vol. 28, No. 3, pp. 530–540, (2015). [5] B. Schweizer, "Dynamics and stability of turbocharger rotors", Arch Appl Mech, Vol. 80, No. 9, pp.1017–1043, (2009). [6] K. Gjika, L. San Andrés, G.D. Larue, “Nonlinear Dynamic Behavior of Turbocharger Rotor-Bearing Systems with Hydrodynamic Oil Film and Squeeze Film Damper in Series: Prediction and Experiment”, J. Comput. Nonlinear Dyn, Vol. 5, No. 4, pp.041006–8, (2010). [7] J.R. Serrano, P. Olmeda, A. Tiseira, L.M. García-Cuevas, A. Lefebvre, “Theoretical and experimental study of mechanical losses in automotive turbochargers”, Energy, Vol. 55, No. 1, pp.888–898, (2013). [8] M. Deligant, P. Podevin, G. Descombes, “Experimental identification of turbocharger mechanical friction losses, Energy”. Vol. 39, No. 1, pp.388–394, (2012). [9] A. Wang, W. Yao, K. He, G. Meng, X. Cheng, J. Yang, "Analytical modelling and numerical experiment for simultaneous identification of unbalance and rolling-bearing coefficients of the continuous single-disc and single-span rotor-bearing system with Rayleigh beam model", Mech Syst Signal Process., Vol. 116, No. 1, pp.322–346, (2019). [10] J. Yao, L. Liu, F. Yang, F. Scarpa, J. Gao, "Identification and optimization of unbalance parameters in rotor-bearing systems", J Sound Vib., Vol. 431, No. 1, pp. 54–69, (2018). [11] A. von Flotow, M. Mercadal, P. Tappert, “Health monitoring and prognostics of blades and disks with blade tip sensors”, "IEEE Aerosp. Conf. Proc.”, Montana, USA, Vol.6, pp. 433–440, (2000). [12] M. Holzenkamp, J.R. Kolodziej, S. Boedo, S. Delmontte, “Seeded Fault Testing and Classification of Dynamically Loaded Floating Ring Compressor Bearings”, ASCE-ASME J. Risk Uncertain. Eng. Syst. Part B Mech. Eng, Vol. 2, No. 2, pp.021003-1, (2016). [13] N.G. Pantelelis, A.E. Kanarachos, N. Gotzias, “Neural networks and simple models for the fault diagnosis of naval turbochargers”, Math. Comput. Simul, Vol. 51, No. 3-4, pp.387–397, (2000). [14] T.H. Machado, R.U. Mendes, K.L. Cavalca, “Directional frequency response applied to wear identification in hydrodynamic bearings”, Mech. Res. Commun, Vol. 74, No. 1, pp.60–71, (2016). [15] N.H. Chandra, A.S. Sekhar, "Wavelet transform based estimation of modal parameters of rotors during operation", Measurement, Vol. 130, No. 1, pp. 264–278, (2018). [16] A. Vencl, A. Rac, “Diesel engine crankshaft journal bearings failures: Case study, Eng. Fail. Anal, Vol. 44, No. 1, pp.217–228, (2014). [17] T.H. Machado, and K.L. Cavalca, “Modeling of hydrodynamic bearing wear in rotor-bearing systems”, Mech. Res. Commun, Vol.69, No. 1, pp.15–23, (2015). [18] S. Chatterton, P. Pennacchi, A. Vania, “Electrical pitting of tilting-pad thrust bearings: Modelling and experimental evidence”, Tribol. Int. Vol. 103, No. 1, pp.475–486, (2016). [19] L. Barelli, G. Bidini, F. Bonucci, “Diagnosis methodology for the turbocharger groups installed on a 1 MW internal combustion engine”, Appl. Energy. Vol. 86, No. 12, pp.2721–2730, (2009). [20] J.R. Serrano, C. Guardiola, V. Dolz, M.A. López, F. Bouffaud, “Study of the turbocharger shaft motion by means of infrared sensors”, Mech. Syst. Signal Process. Vol. 56-57, No. 1, pp.246–258, (2015). [21] Y. Li, F. Liang, Y. Zhou, S. Ding, F. Du, M. Zhou, J. Bi, Y. Cai, “Numerical and experimental investigation on thermohydrodynamic performance of turbocharger rotor-bearing system”, Appl. Therm. Eng. Vol. 121, No. 5, pp.27–38, (2017). [22] L. Shao, J. Zhu, X. Meng, X. Wei, X. Ma, “Experimental study of an organic Rankine cycle system with radial inflow turbine and R123”, Appl. Therm. Eng. Vol. 124, No. 1, pp.940–947, (2017). [23] P. Novotný, P. Škara, J. Hliník, "The effective computational model of the hydrodynamics Journal floating ring bearing for Simulations of long transient regimes of turbocharger rotor dynamics", Int. J. Mech. Sci. Vol. 148, No. 1, pp.611-619, (2018). [24] W. Li, Y. Yang, D. Sheng, J. Chen, “A novel nonlinear model of rotor/bearing/seal system and numerical analysis”, Mech. Mach. Theory. Vol. 46, No. 5, pp.618–631, (2011). [25] M. Dakel, S. Baguet, R. Dufour, “Nonlinear dynamics of a support-excited flexible rotor with hydrodynamic journal bearings”, J. Sound Vib, Vol. 333, No. 10, pp.2774–2799, (2014). [26] P. Konar, and P. Chattopadhyay, “Multi-class fault diagnosis of induction motor using Hilbert and Wavelet Transform”, Appl. Soft Comput. Vol. 30, No. 1, pp.341–352, (2015). [27] Viscopedia \| A free encyclopedia for viscosity. http://www.viscopedia.com/ (accessed September 20, 2016). [28] S.G. Mattson, and S.M. Pandit, “Statistical moments of autoregressive model residuals for damage localisation, Mech. Syst. Signal Process, Vol. 20, No. 3, pp.627–645, (2006). [29] R. Rojas, Neural Networks, Springer Berlin Heidelberg, Berlin, Heidelberg, (1996).
آمار تعداد مشاهده مقاله: 1,333 تعداد دریافت فایل اصل مقاله: 1,453

سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب

پیوندهای مفید

آمار

Identification of rotor bearing parameters using vibration response data in a turbocharger rotor