Development and verification of frequency domain solution methods for rotor-bearing system responses caused by rolling element bearing waviness. (15th January 2022) - Choudhury, Tuhin
Highlights: Two frequency domain solution methods to solve response caused by bearing waviness. The methods consider bearing waviness as base excitation causing forced displacement. In the case study, measured inner ring waviness is used as excitation source. Responses are compared with simulated time domain solutions and measured responses. Proposed methods are computationally efficient and applicable for rolling element bearings. Abstract: In rotordynamic simulations, rolling element bearing waviness is often accounted using nonlinear models that are solved with a numerical integration scheme in time domain. This approach generates accurate system response, but the method is limited in terms of computational efficiency. This study proposes two novel methods for solution of the responses caused by the bearing waviness excitation in frequency domain, and compares the result with a previously developed, time domain based numerical simulation. The first method known as Base Excitation Method (BEM) considers the waviness as base excitation whereas the second method, known as Bearing Kinematics Augmented Base Excitation Method (BKA-BEM), utilizes a four degree of freedom, quasi-static model to include the bearing kinematics and refine the base excitations due to waviness. The methods are validated with a test case, in which measured low order waviness components of the bearing inner ring roundness profile were used as source for excitation. The accuracy and robustness of the proposed methods in calculating the subcritical harmonic response frequencies and amplitudes are examined for different roundness profiles. The results show that the proposed methods performed relatively well compared to previously developed, time domain solution based numerical model and experimental results. Furthermore, the frequency domain solutions significantly reduce the computational time which makes them easily applicable to simulation-based transfer learning, iterative inverse problems and optimization solutions.