An Embedded System for Stator Short-Circuit Diagnosis in Three-Phase Induction Motors Using Information Theory and Artificial Neural Networks
提出一种基于互信息测量和人工神经网络的嵌入式硬件系统，用于三相感应电机定子绕组短路诊断，通过提取电流信号特征并分类，实验验证了其鲁棒性和效率。
This study presents an embedded system in hardware based on mutual information measurements and artificial neural networks for the stator winding short-circuit diagnosis of three-phase induction motors (TIMs) with a line-connected sinusoidal power supply. The methodology employs an information theory measure to extract the most relevant characteristics of the current signals of TIM phases A and B. These data are presented to a multilayer perceptron neural network that performs the pattern classification. Experimental tests with different machine operating conditions validate the robustness and efficiency of the proposed methodology.