基于无监督迁移学习的核范数最大化轴承故障诊断方法

Fault Diagnosis of Bearing Based on Nuclear-norm Maximization and Unsupervised Learning

针对实际工业运行中带标签的轴承故障数据难以获取,导致有监督学习故障诊断效果不佳的问题,提出一种基于无监督迁移学习(transfer learning,TL)的核范数最大化轴承故障诊断方法。该方法通过结构优化深度卷积神经网络(structure optimized deep convolutional neural networks,SOCNN)进行故障特征提取,利用最大均值差异(maximum mean discrepancy,MMD)提升源域和目标域的分布相似度,并结合快速批量核范数最大化(fast batch nuclear-norm maximization,FBNM)来提升目标域批量输出矩阵的可分辨性和多样性。实验结果表明:所提方法在不同噪声环境中都具有较高的诊断精度,能准确识别出轴承的故障类型和故障危害等级,为轴承故障诊断提供有效技术支撑。

Aiming at the problem that it is difficult to obtain bearing fault data with labels in actual industrial operation,which leads to the poor effect of supervised learning fault diagnosis,a bearing fault diagnosis method combining unsupervised transfer learning(TL)and kernel norm maximization was proposed.This method extracts fault features through structure optimized deep convolutional neural networks(SOCNN),and uses the maximum mean difference(MMD)to improve the distribution similarity between source domain and target domain,The fast batch nuclear norm maximization(FBNM)was combined to improve the distinguishability and diversity of the target domain batch output matrix.The experimental results show that the proposed method has high diagnostic accuracy in different noise environments,can accurately identify the fault types and hazard levels of bearings,and provides effective technical support for bearing fault diagnosis.