基于SACNN-MGRU混合模型的滚动轴承故障诊断

Fault diagnosis of rolling bearing based on SACNN-MGRU hybrid model

为充分挖掘滚动轴承故障类别与振动信号间的潜在联系进而提升故障诊断精度,提出了一种基于尺度自适应卷积神经网络(SACNN)和改进门控循环单元(MGRU)混合模型的故障诊断方法。首先,提出了一种尺度自适应因子用以获取合适的CNN窗口尺寸从而更有效地提取振动信号中蕴含的局部故障信息,并在CNN中引入比例指数线性单元(SELU)以提升其训练过程的鲁棒性;随后,在GRU中嵌入SELU进一步提升网络稳定性,并改进GRU网络结构增强其时序特征的挖掘能力,进而更充分地提取局部故障信息中的时序特征;最后通过Softmax函数识别故障类别。经实验对比和分析表明,该方法具备较好的收敛性和稳定性,能够有效挖掘振动信号中蕴含的故障信息,准确识别不同转速下滚动轴承的故障类别且识别精度均高于99.5%,具有一定的应用价值。

In order to fully explore the potential connection between the rolling bearing fault types and the vibration signal to improve the diagnosis accuracy,a fault diagnosis method based on the hybrid model of scale adaptive convolutional neural network(SACNN)and modified gated recurrent unit(MGRU)is proposed.To begin with,a scale adaptive factor is proposed to obtain appropriate CNN window size for extracting local fault information from the raw signal more effectively,and scaled exponential liner unit(SELU)is introduced into CNN to improve the robustness of its training process.Subsequently,SELU is embedded into GRU to further enhance the network stability and the network structure of GRU is ameliorated to enhance the temporal feature extraction ability,thereby extracting temporal feature from the local fault information more fully.Finally,the softmax function is applied for recognizing fault types.The experimental comparison and analysis reveal that the proposed method achieves better convergence and stability,can effectively mine the fault information contained in the vibration signal for accurately recognizing the rolling bearing fault types at different speeds with the recognition accuracies higher than 99.5%,which has certain application value.