齿轮箱是机械设备中用于连接和传递动力的零部件,由于受到自身结构较为复杂,所工作环境较为恶劣等的因素,常常会发生一些故障性的问题。本研究采用深度学习方法进行齿轮箱的故障的人工智能识别模型建立和分析,并将深度学习预测结果与传统BP神经网络和支持向量机预测结果进行对比。实验分析结果表明,采用深度学下的方法其对齿轮箱的故障识别的准确率为93.3%,优于BP神经网络和支持向量机方法。可有效的应用于齿轮箱故障诊断。 Gearbox is a mechanical equipment used to connect and transmit power components, due to its own complex structure, the working environment is more adverse factors, often occur some faulty problems. In this study, a deep learning method was used to establish and analyze the artificial intelligence recognition model of gearbox fault, and the results of deep learning prediction were compared with those of traditional BP neural network and support vector machine. The experimental results show that the accuracy of gearbox fault identification using the method of deep learning is 93.3%, which is better than BP neural network and support vector machine. Can be effectively used in gearbox fault diagnosis.