The design of induction machine is a challenging task due to different electromagnetic and thermal constraints. Quick estimation of machine's dimensions is important in the sales tool to provide quick quotations to customers based on specific requirements. The key part of this process is to select different design parameters like length, diameter, tooth tip height and winding turns to achieve certain torque, current and temperature of the machine. Electrical machine designers, with their experience know how to alter different machine design parameters to achieve a customer specific operation requirements. We propose a reinforcement learning algorithm to design a customised induction motor. The neural network model is trained off-line by simulating different instances of of electrical machine design game with a reward or penalty function when a good or bad design choice is made. The results demonstrate that the suggested method automates electrical machine design without applying any human engineering knowledge.

翻译：感应电机设计因涉及不同的电磁和热约束而极具挑战性。在销售工具中快速估算电机尺寸至关重要，这有助于根据客户特定需求快速提供报价。此过程的关键在于选择不同的设计参数，例如长度、直径、齿尖高度和绕组匝数，以实现电机的特定扭矩、电流和温度。电机设计人员凭借其经验，知道如何调整不同的电机设计参数以满足客户特定的运行要求。我们提出了一种强化学习算法来设计定制化感应电机。该神经网络模型通过模拟电机设计游戏的多个实例进行离线训练，当做出良好或不良的设计选择时，会分别给予奖励或惩罚函数。结果表明，所提出的方法无需任何人类工程知识即可自动完成电机设计。