摘要
深度学习在滚动轴承故障诊断中具有广泛的应用,然而,现实中的监测数据往往具有不平衡性,这就会对模型的诊断性能产生很大影响。因此,提出一种基于改进门控卷积神经网络(Improved Gated Convolutional Neural Network,IGCNN)的故障诊断方法,用于数据不平衡条件下的故障诊断。首先,提出改进门控卷积层以增强特征提取能力,通过批量归一化技术提高模型的泛化能力。然后,使用标签分布感知边界(Label-distribution-aware Margin,LDAM)损失函数提高模型对少数类的敏感度,减小数据不平衡对模型的影响。将所提算法应用在两组故障轴承数据上,在数据不平衡率为20:1的情况下,所提算法仍然可达到92.71%和94.47%的故障识别率,而对比的其他主流深度学习模型在该情况下只有60%~72%的准确率,表明所提方法在数据集严重不平衡情况下具有很强的诊断能力和鲁棒性。
Intelligent methods based on deep learning have been widely used in the fault diagnosis of rolling bearings.However,the actual data from industrial plant is severely imbalanced,which will greatly affect the diagnostic performance of the model.To solve this problem,a fault diagnosis method based on improved gated convolutional neural network(IGCNN)is proposed.Firstly,an improved gated convolution layer is proposed for feature extraction,with the batch normalization layer applied in this layer to enhance the generalization performance of the model.Then,the label-distributionaware margin(LDAM)loss function is employed to raise the sensitivity of the model to the minority class and mitigate the influence of imbalanced data on the model.The proposed method is applied to two sets of faulty bearing data.It is found that when the imbalance ratio of the data sets is 20:1,the recognition accuracy of the proposed method can still achieve 92.71%and 94.47%.While the other compared mainstream deep learning models only achieve 60%-72%accuracy.It shows that the proposed method has strong diagnostic capability and robustness for the datasets with severe imbalance.
作者
郗昌盛
梁小夏
田少宁
杨杰
冯国金
甄冬
XI Changsheng;LIANG Xiaoxia;TIAN Shaoning;YANG Jie;FENG Guojin;ZHEN Dong(School of Mechanical Engineering,Hebei University of Technology,Tianjin 300401,China;Advanced Equipment Research Institute Co.,Ltd.,Hebei University of Technology,Tianjin 300401,China)
出处
《噪声与振动控制》
CSCD
北大核心
2024年第4期153-160,共8页
Noise and Vibration Control
基金
国家自然科学基金资助项目(52275101)
天津市自然科学基金资助项目(21JCZDJC00720)
2022年河北省春晖计划资助项目(E2022202101)。
