双通道信息融合的机械旋转部件混合故障诊断

Hybrid fault diagnosis of mechanical rotating parts based on double channel information fusion

为了实现机械旋转部件的混合故障诊断具有较高准确性,提出一种变工况下双通道信息融合的旋转部件混合故障诊断方法。其信号同时包含滚动轴承和齿轮振动信号。通道1的振动信号进行广义S变换生成二维信号,以特征图作为通道1的模型输入;通道2将旋转部件的时域信号作为特征输入,双通道输出层随机特征融合,通过对整个双通道卷积神经网络(CNN)模型参数的微调,实现变工况下旋转部件混合故障状态的诊断识别。结果表明,所提方法能够有效地运用于旋转部件混合故障识别诊断,与一维、二维卷积神经网络以及其他机器学习方法对比,所提方法故障识别准确率最高,达到98.18%。

In order to achieve high accuracy in hybrid fault diagnosis of rotating parts, a hybrid fault diagnosis method based on information fusion of two channels under variable working conditions is proposed. The signal includes both rolling bearing and gear vibration signal. The vibration signal of channel 1 is generated by generalized S transform, and the feature map is used as the model input of channel 1. In channel 2, the time-domain signals of the rotating parts are taken as the characteristic input, and the two-channel output layer is randomly fused with the features. By fine-tuning the parameters of the whole two-channel convolutional neural network(CNN) model, the diagnosis and identification of the mixed fault state of the rotating parts under varying working conditions are realized. The results show that the proposed method can be effectively applied to the mixed fault identification and diagnosis of rotating parts. Compared with the one-dimensional and two-dimensional convolutional neural network and other machine learning methods, the proposed method has the highest fault identification accuracy, reaching 98.18%.