Identification of Linearized Dynamic Characteristics of Rolling Element Bearings

Abstract

This study presents a new method for estimating linearized coefficients of rolling element bearings. The rotor-bearing systems considered here are composed of a flexible shaft, which may be changed stepwise along the axial direction, and multiple rigid disks supported on anisotropic bearings. The main feature of this identification technique is that it eliminates the need to measure the external input force. The estimator is constructed by using the relations of unbalance responses and known system parameters to estimate the coefficients of rolling element bearings incorporated with the least-squares method without using an input force device. The theoretical development of the procedures is presented together with simulation and experimental results.

An adaptive hybrid differential evolutionary algorithm for the parameter identification of rotating machinery

“…The impulse response measurement techniques were used to identify the residual unbalance and bearing dynamic parameters (Tiwari et al., 2002; Tiwari and Chakravarthy, 2006). Chen and Lee also proposed an impulse or synchronous/nonsynchronous excitation technique for estimating the linear coefficients of bearings (Chen and Lee, 1995; 1997).…”

Parameter identification of nonlinear rotor-bearing system based on improved kriging surrogate model

“…As an alternative technique, one of the most traditional optimization algorithms is least squares method (Chen and Lee, 1995). Chen and Lee (1997) formulated a minimization problem between measured unbalance response and the estimated response and then the least squares method was used as an optimizer. However, from other studies (Edwards et al., 2000; Kim et al., 2007), the noise (such as rotor bow, misalignment) existing in the test data results in the local minima problem and the nonlinear affects (such as nonlinear rub-impact, oil-film force of the journal bearing) results in a nonlinear relationship between the dynamic parameters, which means the least square optimizer cannot guarantee global minimum and the identified results may not be the best ones in the rotor system.…”

Identification of Speed-Dependent Bearing Parameters

“…Sahinkaya and Burrows [7] and Tieu and Qiu [8] estimated the linearized oil "lm parameters from the out-of-balance response where the shaft was excited by a known unbalance force (synchronous excitation). Chen and Lee [9] identi"ed rolling element dynamic characteristics in #exible rotor-bearing systems by using unbalance responses at all bearings and several shaft locations without a priori knowledge of the unbalance. Muszynska and Bently [10] developed a perturbation technique (two frequency swept periodic inputs) for estimation of these parameters.…”