基于1-DCNN-LSTM的滚动轴承自适应故障诊断方法研究

Adaptive fault diagnosis method for rolling bearings based on 1-DCNN-LSTM

针对滚动轴承故障振动信号的非线性和非平稳特征,提出了一种自适应的一维卷积神经网络(1-Dimensional Convolutional Neutral Networks,1-DCNN)和长短期记忆网络(Long Short-Term Memory,LSTM)融合的轴承故障诊断方法。首先,将原始一维振动信号通过有重叠取样的方式分别输入1-DCNN和LSTM两个通道,然后通过Concatenate层进行空间和时间维度上特征信息的融合,最后,通过Softmax分类器进行故障类别的分类输出。该方法可以直接从原始振动信号中自适应提取特征,实现了"端到端"的故障诊断。采用CTU-2实验平台故障数据,通过对滚动轴承的不同故障类型、不同传感器采集方位、不同故障直径进行实验分析,结果表明:该方法在识别轴承故障类别上与其他方法相比具有更高的识别精度,并具有良好的有效性和稳定性。

Aiming at the non-linear and non-stationary characteristics of rolling bearing fault vibration signals,this paper proposes an adaptively bearing fault diagnosis method based on one-dimensional convolutional neural network(1-DCNN)and long-short-term memory(LSTM)network.Firstly,the original one-dimensional vibration signals are input into the two channels of 1-DCNN and LSTM by overlapping sampling.Then they fuse the feature information of spatial and temporal dimensions through the concatenate layer.Finally,the classifications of the fault categories are performed by the softmax classifier.The method can extract features from the original vibration signal adaptively and achieve"end-to-end"fault diagnosis.The experimental analysis of rolling bearings of different fault types,different sensor acquisition orientations and different fault diameters shows that the proposed method has higher recognition accuracy compared with other methods in identifying bearing fault categories,and has good adaptability and effectiveness.