Motor fault diagnosis method based on deep learning
基于深度学习的电机故障诊断方法

摘要

随着电机在各领域广泛应用，其故障诊断至关重要。传统方法有局限，深度学习凭借强大特征学习能力崭露头角。通过构建合适神经网络模型，采集电机运行多源数据，如振动、电流等，经数据预处理、模型训练优化，能精准识别电机各类故障模式，有效提升诊断准确率与效率，为保障电机可靠运行提供有力技术支撑。

Abstract

As motors are widely applied in various fields, fault diagnosis has become crucial. Traditional methods have limitations, while deep learning, with its powerful feature learning capabilities, is emerging as a promising approach. By constructing appropriate neural network models and collecting multi-source data from motor operation, such as vibration and current, through data preprocessing and model training optimization, it can accurately identify various fault modes of motors. This effectively enhances diagnostic accuracy and efficiency, providing strong technical support for ensuring the reliable operation of motors.