The stator current signals of mining asynchronous motor are often non-stationary, making it challenging to extract fault features in the time domain. Therefore, this paper proposes a rotor fault diagnosis method based on the combination of Modified Ensemble Empirical Mode Decomposition (MEEMD) energy entropy and Artificial Neural Network (ANN). Firstly, the stator current signals are decomposed into a series of Intrinsic Mode Function (IMF) components by the MEEMD. Secondly, the IMF components with the most abundant information are selected by the cross-correlation criterion, and their energy entropy is calculated to construct feature vectors. Finally, the feature vectors are input into the ANN for training and state recognition. The faulty motor is modeled by ANSYS Maxwell software to obtain the simulated data. It is verified that the MEEMD-ANN method is feasible for fault diagnosis of mine motors, which can accurately identify the different status of motors, including normal state, broken rotor bars, and air gap eccentricity, the recognition rate can reach 99%. The MEEMD-ANN improves the accuracy by 2% compared with the EEMD-ANN, improves the accuracy by 3.75% compared with the MEEMD-SVM.

采矿异步电动机的定子电流信号通常是不稳定的，这使得在时域提取故障特征具有挑战性。因此，本文提出了一种基于改进的集成经验模态分解能量熵和人工神经网络相结合的转子故障诊断方法。首先，定子电流信号通过MEEMD分解为一系列本征函数（IMF）分量。其次，通过互相关准则选择信息最丰富的IMF分量，并计算它们的能量熵以构建特征向量。最后，将特征向量输入到ANN中进行训练和状态识别。通过ANSYS Maxwell软件对故障电动机进行建模，以获得仿真数据。证明了MEEMD-ANN方法对矿山电机的故障诊断是可行的，能准确识别出电机的不同状态，包括正常状态，转子断条和气隙偏心率，识别率可达99％。与EEMD-ANN相比，MEEMD-ANN的精度提高了2％，与MEEMD-SVM相比，MEEMD-ANN的精度提高了3.75％。