为提高感应电机状态评估的精度，本研究提出了一种基于相关性算法( CF) 和自 组织映射最小量化误差( SOM-MQE) 的模型来解决基波电流信号干扰和缺少故障数据的 问题． 首先简要介绍自相关算法与互相关算法理论，分析了定子电流中的特征谐波分量， 将其作为性能退化指标输入 SOM 神经网络中，在此基础上计算其最小量化误差( MQE) 值的大小，并将 MQE 作为感应电机状态监测的衡量指标． 实例表明，所提模型能够准确地 对感应电机健康状态进行估计，具有较强的工程应用价值及通用性

To improve the accuracy of induction motor state evaluation，a model based on correlation algorithm ( CF) and self-organizing map minimum quantization error ( SOM-MQE) was proposed to solve the problem of fundamental current signal interference and lack of fault data． Firstly，the autocorrelation algorithm and cross-correlation algorithm theory were briefly introduced． The characteristic harmonic components in the stator current were analyzed and input into the SOM neural network as performance degradation indicators． Based on this，the minimum quantization error ( MQE) value was calculated． MQE was used as a measure of condition monitoring of induction motors． The example shows that the proposed model can accurately estimate the health status of induction motors，so it has strong engineering application value and versatility．