Vibratory behavior reduction of electrical machines through materials properties evaluation

Most of the electromagnetic devices, especially electrical machines, have the disadvantage to be exposed to high vibrations caused by magnetic forces. The aim of this study is to propose a methodology to optimize the cylindrical stators generally used in electrical machines regarding the vibration phenomena. Techniques for vibration reduction require knowledge of the proper frequencies, which depend on mechanical shapes and dimensions as well as material properties such as mass density, Young's modulus and Poisson's ratio. This paper proposes a new approach which is based on the identification of mass density (lamination stacking factor) and Young's modulus in the goal to minimize the vibratory behavior of electrical machines. In this goal, we have used artificial intelligent and finite element method (FEM) analysis to solve the magneto‐mechanical inverse problem (IP). In the proposed approach, a Multilayer Perceptron Neural Network (MLPNN) is used as a forward model in order to decrease the FEM time consuming. Thus, a Genetic Algorithm (GA) is used to solve the IP in a reasonable time of running. An example study of an induction machine proves that the developed approach may be applied in both design and identification applications. Copyright © 2011 John Wiley & Sons, Ltd.