This paper presents an optimal method for rotordynamic simulation of induction motors, including the effects of unbalanced magnetic pull (UMP). The developed simulation method containing the UMP model is simple but still accurate for the actual design process of induction motors. The UMP model is simplified by using the magnetizing current without calculation of the rotor current. The effects of the slot opening and saturation are initially incorporated into the model by using the Carter factor. To improve the accuracy of the model, the magnetizing current is calculated by the finite element analysis (FEA), and the proposed correction factor is also built into the model. Moreover, mixed eccentricity is modeled and applied to the time step rotordynamic simulation for considering the actual rotor eccentricity condition. Based on the developed UMP and eccentricity models, rotordynamic simulation methods within the induction motor design process are proposed and tested in a standard four-pole induction motor. The simulation results show that inclusion of the UMP force reduces the critical speeds and generates electromagnetic excitation. The study further shows that the effects of UMP vary with a change in static eccentricity, dynamic eccentricity, slip, and bearing stiffness. Finally, based on the results, a utilization plan of the developed methods is proposed.

In large rotor-bearing systems, the rolling element bearings act as a considerable source of subcritical vibration excitation. Simulation of such rotor bearing systems contains major sources of uncertainty contributing to the excitation, namely the roundness profile of the bearing inner ring and the clearance of the bearing. In the present study, a simulation approach was prepared to investigate carefully the effect of varying roundness profile and clearance on the subcritical vibration excitation. The FEM-based rotor-bearing system simulation model included a detailed description of the bearings and asymmetricity of the rotor. The simulation results were compared to measured responses for validation. The results suggest that the simulation model was able to capture the response of the rotor within a reasonable accuracy compared to the measured responses. The bearing clearance was observed to have a major effect on the subcritical resonance response amplitudes. In addition, the simulation model confirmed that the resonances of the 3rd and 4th harmonic vibration components in addition to the well-known 2nd harmonic resonance (half-critical resonance) can be significantly high and should thus be taken into account already in the design phase of large subcritical rotors.

Design of High Speed (HS) electric machines is an iterative process that requires a multidisciplinary design team to accomplish the required performance. In this study, a design space method (DSM) is developed to streamline conceptual designing of a high-speed and high-power electric machine. The method uses analytical equations and a rotordynamic model to determine geometrical dimensions based on the application requirements. These dimensions create a feasible baseline design for the particular application. However, considering the dimensions as design variables and using the baseline design as a starting point, a multidimensional combination and interaction of the design variables and the correlated output for the particular topology of motor and performance range can be further studied for design exploration and optimization purposes. The study includes a test case where the baseline dimensions are determined and compared to an existing machine from literature, and then further explored to identify the sensitivity of different outputs with respect to different design variables. The method enables rapid design iterations, rotordynamics and rotor mass optimization. The baseline design can be also used as a starting point for the detailed design.

Development and verification of frequency domain solution methods for rotor-bearing system responses caused by rolling element bearing waviness, Mechanical Systems and Signal Processing, 163 (2022) 108117.

The mounting of a rotating machine affects the dynamic behavior of the machine. Typically in large machines, the support structures have lower stiffness on the actual site than in the acceptance tests conducted by the manufacturers. In this research, a method is developed for the support stiffness identification for an in-situ machine using a simulation-data-driven, deep learning algorithm. The novel approach aims to utilize transfer learning to first teach the deep learning algorithm using vibration response data generated from a simulation model of the rotor-bearing-support system, and then test it with measured response. To validate the stiffness estimation of the algorithm for multiple cases, an experimental test rig is used where the horizontal support stiffness can be varied through a range of values. The results from the deep learning algorithm are compared with simpler algorithms such as Linear regression (LR), Artificial Neural Network (ANN), and Support vector regression (SVR) for benchmarking. The models are trained with filtered frequency domain response, and challenges due to measurement uncertainty are analyzed. With proposed pre-processing steps of the frequency domain response and outlier elimination, the deep learningbased virtual sensor can predict the support stiffness with reasonable accuracy, where the limiting factor is the data quality and lack of excitation at critical speed frequencies.