High-Efficiency Equivalent Material Parameter Identification for Motor Key Components: Toward Accurate Vibration Calculation

The accurate acquisition of motor equivalent material parameters forms the basis for precise vibration and noise analysis. However, most motor key components, such as stator and rotor, are made from multiple materials, posing a challenge to the efficient and accurate acquisition of equivalent material parameters for these components. This article employs the finite element method (FEM) to analyze the modal characteristics and identifies the optimal equivalent material parameters of motor components by combining the proposed improved particle swarm optimization (IPSO) algorithm with modal testing results. A modal order identification algorithm (MOIA) is proposed, which can automatically identify the modal shapes based on the finite element modal analysis results, thereby determining the target modal orders and frequencies. The multiobjective fitness function (MOFF) and adaptive weight coefficients have been developed, enabling effective handling of multiple target modes optimization during the equivalent material parameter identification. The sparse population particle swarm optimization (SPPSO) algorithm has been proposed not only to enhance computational efficiency but also to address the issue of sparse populations easily getting trapped in local optima. The automatic identification of equivalent material parameters is achieved using the IPSO–FEM iterative calculation method. Finally, experiments demonstrate that the optimal equivalent material parameters identified by the IPSO–FEM method can effectively enhance the accuracy of motor vibration calculation.