基于改进卷积神经网络的行星齿轮箱故障诊断研究

Research on fault diagnosis of planetary gearbox based on improved convolutional neural network

行星齿轮箱作为机械设备的重要传动部件,其运行的好坏直接影响到整个设备的运行状况。通过引入批量归一化层和丢弃层对卷积神经网络模型进行改进,提出了基于改进卷积神经网络的齿轮箱故障诊断模型。搭建齿轮箱实验平台,使用该模型对齿轮箱的振动信号进行故障识别。实验结果表明:该模型能够有效地对齿轮箱不同的故障类型进行识别分类,分类准确率达到了99.2%。

Planetary gearbox is an important transmission component of mechanical equipment,and its operation directly affects the operating status of the entire equipment.The convolutional neural network model is improved by introducing the batch normalization layer and the discarding layer,and a gearbox fault diagnosis model based on the improved convolutional neural network is proposed.Set up a gearbox experiment platform,and use the model to identify faults in the gearbox vibration signal.The experimental results show it can be known that the improved model can effectively identify and classify the different types of gearbox faults.The classification accuracy rate reached 99.2%.