A Novel Method for the Dynamic Coefficients Identification of Journal Bearings Using Kalman Filter

The dynamic coefficients identification of journal bearings is essential for instability analysis of rotation machinery. Aiming at the measured displacement of a single location, an improvement method associated with the Kalman filter is proposed to estimate the bearing dynamic coefficients. Firstly, a finite element model of the flexible rotor-bearing system was established and then modified by the modal test. Secondly, the model-based identification procedure was derived, in which the displacements of the shaft at bearings locations were estimated by the Kalman filter algorithm to identify the dynamic coefficients. Finally, considering the effect of the different process noise covariance, the corresponding numerical simulations were carried out to validate the preliminary accuracy. Furthermore, experimental tests were conducted to confirm the practicality, where the real stiffness and damping were comprehensively identified under the different operating conditions. The results show that the proposed method is not only highly accurate, but also stable under different measured locations. Compared with the conventional method, this study presents a more than high practicality approach to identify dynamic coefficients, including under the resonance condition. With high efficiency, it can be extended to predict the dynamic behaviour of rotor-bearing systems.

[1]  Feng Gao,et al.  A Kalman-filter based time-domain analysis for structural damage diagnosis with noisy signals , 2006 .

[2]  She-Xu Zhao,et al.  Experimental identification of linear oil-film coefficients using least-mean-square method in time domain , 2005 .

[3]  Venkata Dinavahi,et al.  Extended Kalman Filter-Based Parallel Dynamic State Estimation , 2016, IEEE Transactions on Smart Grid.

[4]  Bo-Suk Yang,et al.  Bearing parameter identification of rotor–bearing system using clustering-based hybrid evolutionary algorithm , 2007 .

[5]  Chin-Hsiung Loh,et al.  Input force identification using Kalman filter techniques: application to soil-pile interaction , 2008, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[6]  Giorgio Battistelli,et al.  Stability of consensus extended Kalman filter for distributed state estimation , 2016, Autom..

[7]  Arthur W. Lees,et al.  Identification of Dynamic Bearing Parameters: A Review , 2004 .

[8]  Fang Han,et al.  Bearing parameter identification of rotor-bearing system based on Kriging surrogate model and evolutionary algorithm , 2013 .

[9]  Gunhee Jang,et al.  A generalized Reynolds equation and its perturbation equations for fluid dynamic bearings with curved surfaces , 2012 .

[10]  Gerd Wessolek,et al.  Estimating Pore Water Electrical Conductivity of Sandy Soil from Time Domain Reflectometry Records Using a Time-Varying Dynamic Linear Model , 2018, Sensors.

[11]  Luis San Andrés,et al.  Experimental Identification of Bearing Dynamic Force Coefficients in A Flexible Rotor—Further Developments , 2007 .

[12]  Minel J. Braun,et al.  Comparison of Perturbed Reynolds Equation and CFD Models for the Prediction of Dynamic Coefficients of Sliding Bearings , 2018 .

[13]  Xuegang Song,et al.  Load Identification for a Cantilever Beam Based on Fiber Bragg Grating Sensors , 2017, Sensors.

[14]  Weiwen Deng,et al.  The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation , 2016, Sensors.

[15]  H. D. Nelson.  A Finite Rotating Shaft Element Using Timoshenko Beam Theory , 1980 .

[16]  Rajiv Tiwari,et al.  Simultaneous identification of residual unbalances and bearing dynamic parameters from impulse responses of rotor-bearing systems , 2006 .

[17]  C. Papadimitriou,et al.  A dual Kalman filter approach for state estimation via output-only acceleration measurements , 2015 .

[18]  Helio Fiori de Castro,et al.  Bayesian inference applied to journal bearing parameter identification , 2017 .

[19]  Claudio E. Merelli,et al.  Dynamic coefficients of finite length journal bearing. Evaluation using a regular perturbation method , 2019, International Journal of Mechanical Sciences.

[20]  Jianping Jing,et al.  Identification of dynamic coefficients in circular journal bearings from unbalance response and complementary equations , 2018, Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology.

[21]  Amiya R Mohanty,et al.  Estimation of single plane unbalance parameters of a rotor-bearing system using Kalman filtering based force estimation technique , 2018 .

[22]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[23]  Shuo Zhang,et al.  Stiffness and damping coefficients for journal bearing using the 3D transient flow calculation , 2017 .

[24]  Chen Zhao,et al.  Active magnetic bearings dynamic parameters identification from experimental rotor unbalance response , 2017 .

[25]  Li-Wei Fong,et al.  AN INPUT ESTIMATION APPROACH TO ON-LINE TWO-DIMENSIONAL INVERSE HEAT CONDUCTION PROBLEMS , 1996 .

[26]  Kun Li,et al.  Identification of oil-film coefficients for a rotor-journal bearing system based on equivalent load reconstruction , 2016 .

[27]  Anh Kiet Tieu,et al.  Identification of sixteen force coefficients of two journal bearings from impulse responses , 1997 .

[28]  Imre J. Rudas,et al.  Kalman filter for mobile-robot attitude estimation: Novel optimized and adaptive solutions , 2018, Mechanical Systems and Signal Processing.

[29]  Rajiv Tiwari,et al.  Simultaneous estimation of the residual unbalance and bearing dynamic parameters from the experimental data in a rotor-bearing system , 2009 .

[30]  Chih-Kao Ma,et al.  Input forces estimation of a cantilever beam , 2000 .

[31]  M. Byrtus,et al.  Dynamic coefficients and stability analysis of finite-length journal bearings considering approximate analytical solutions of the Reynolds equation , 2019, Tribology International.

[32]  Alon Kuperman,et al.  Recursive-Least-Squares-Based Real-Time Estimation of Supercapacitor Parameters , 2016, IEEE Transactions on Energy Conversion.

[33]  Brian K. Weaver,et al.  Model-based interpolation-iteration method for bearing coefficients identification of operating flexible rotor-bearing system , 2017 .

[34]  M. Friswell,et al.  IDENTIFICATION OF SPEED-DEPENDENT BEARING PARAMETERS , 2002 .

[35]  Anh Kiet Tieu,et al.  Identification of sixteen dynamic coefficients of two journal bearings from experimental unbalance responses , 1994 .

[36]  Eric Monmasson,et al.  Dual-Kalman-Filter-Based Identification and Real-Time Optimization of PV Systems , 2015, IEEE Transactions on Industrial Electronics.