基于特征差异性学习卷积神经网络的齿轮箱故障诊断方法

Gearbox Fault Diagnosis Method Based on Feature Difference Learning Convolutional Neural Network

针对齿轮箱故障诊断需要大量专家经验知识、人工提取特征困难的问题,提出基于特征差异性学习卷积神经网络(FDLCNN)的故障诊断方法。构建不同深度的多尺度网络,并引入残差模块,以提升网络的特征提取能力;提取一维时序信号中不同尺度不同深度的故障特征,再通过自适应平均池化层处理后进行特征融合,以丰富智能诊断决策信息;最后在全连接层实现特征降维,使用Softmax分类器输出诊断结果。利用10种齿轮箱故障状态实验数据与现有3种方法进行对比分析,结果表明:FDLCNN故障识别精度更高,鲁棒性更强,收敛速度更快。

To solve the problem that gearbox fault diagnosis requires a lot of expert experience and it is difficult to extract features manually,a fault diagnosis method based on feature difference learning convolutional neural network(FDLCNN)was proposed.Multi-scale networks with different depths were constructed and residual modules were introduced to improve the feature extraction capability of the network.Fault features of different scales and depths were extracted from 1D time-series signals,and then feature fusion was carried out through adaptive average pooling layer processing to enrich intelligent diagnosis and decision information.Finally,feature dimension reduction was implemented in the full connection layer,and the diagnostic results were output by the Softmax classifier.The experimental data of ten kinds of gearbox fault states were compared with the existing three methods.The results show that FDLCNN has higher fault identification accuracy,stronger robustness and faster convergence speed.