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

Abstract

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.

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

“…In order to effectively identify the feature information contained in the fault signal of rotating machinery and reveal its inherent characteristics, many fault feature extraction methods of rotating machinery have been proposed, such as empirical mode decomposition (EMD) [7,8], mathematical morphology filtering [9,10], wavelet decomposition [11,12], adaptive filtering [13,14], matching pursuit [15,16], cyclostationary signal analysis [17,18], Wiener filter [19], Kalman filter [20,21], and stochastic resonance [22,23] that are widely used in early fault diagnosis of rotating machinery. The EMD proposed by Huang et al [7] is a nonstationary signal analysis method, which can find the hidden characteristic information in the signal, and has been widely used in the extraction and noise reduction of the impact signal of rotating machinery.…”

“…Pang et al [25] proposed an adaptive filtering algorithm based on mathematical morphology for rolling bearing fault diagnosis. Kang et al [20] proposed an improvement method associated with the Kalman filter to estimate the bearing dynamic coefficients. Cheng et al [26] proposed a novel deconvolution algorithm called adaptive multipoint optimal minimum entropy deconvolution adjusted (AMOMEDA) for extracting fault-related features from noisy vibration signals.…”

Model updating for rotor-discs system and its application in dynamic coefficients identification of journal bearings

“…The dynamic coefficients of journal bearings directly affect the stability performance and unbalance responses of the whole rotor system, including the four stiffness coefficients and four damping coefficients. To acquire these coefficients online, the field identification techniques are commonly adopted based on the FE model of the rotor-bearing system [4,5,6]. In the identification process, one of the significant challenges is to develop a rotor-discs FE model as precisely as possible since an inaccurate model would inevitably cause some errors in the prediction of system dynamic performances.…”

An Algebraic Approach for Identification of Rotordynamic Parameters in Bearings with Linearized Force Coefficients

“…Additionally, in [17], the authors estimated the rotordynamic coefficients of a controllable floating ring bearing with a magnetorheological fluid (MRF) showing that the magnetic field-induced, field-dependent viscosity of the MRF changes the stiffness and damping bearing coefficients, which can be used to modify the dynamic behavior of the rotor-bearing system. In 2020, Kang et al [18] used the Kalman filter to estimate the bearing dynamic coefficients of a flexible rotor-bearing system. The rotor system is modeled with Timoshenko beam elements, but the imbalance force considered in the dynamic model is calculated for a constant rotational velocity condition.…”