目的 为了解决自动化包装生产线针对电机驱动故障诊断复杂化和精度低的问题，提高复杂生产环境下电机运行的稳定和人员的安全，提出一种基于XGBoost特征重构和神经网络预测电机驱动故障的精准预测方法。方法 首先通过XGBoost算法运用一部分训练数据构建特征树，随后将剩余训练数据输入XGBoost算法得到重构的特征，然后再运用One-hot编码，将重构特征映射到欧式空间，进一步放大特征的差异，最后输入经过参数调整的神经网络模型中完成故障预测。结果 相较于未经XGBoost特征构建的神经网络模型，文中提出的结构在数据测试集随机分割的验证集和测试集上均取得了接近100%的分类精度，验证了模型的有效性和稳定性。结论 较好地实现了针对自动化包装生产线电机驱动故障的无传感器高精度诊断，有利于提高复杂生产环境下的电机稳定性和人员安全性。

In order to solve the problem of complex and low accuracy of motor drive fault diagnosis in automatic packaging production line, and to improve the stability of motor operation and personnel safety in complex production environment, a precise prediction method of motor drive fault based on XGBoost feature reconstruction and neural network prediction is proposed. The method first uses a part of the training data to construct a feature tree through the XGBoost algorithm, and then inputs the remaining training data into the XGBoost algorithm to obtain the reconstructed features. Consequently, using the One-hot encoding to map the reconstructed features to the Euclidean space to further amplify the difference in features. Finally, the obtained features are input into the neural network model with parameter adjustment to complete the fault prediction. Compared with the neural network model constructed without XGBoost features, the structure proposed in this paper achieves nearly 100% classification accuracy on the verification set and the test set of the data test set randomly divided, which verifies the effectiveness and stability of the model. The sensorless high-precision diagnosis of the motor drive fault in automatic packaging production line is realized, which is beneficial to improve motor stability and personnel safety in complex production environment.