基于类别域自适应的滚动轴承故障诊断

Bearing fault diagnosis model based on class domain adaptation

滚动轴承作为旋转机械的关键部件,在风电机组中故障频发严重制约了发电效率。而传统的滚动轴承故障诊断方法要求训练数据和测试数据服从同一分布,导致其泛化能力不足,并不能有效解决实际工业中的无标签跨域故障诊断问题。为此,提出了一种基于类别域自适应的轴承故障诊断方法,利用有标签的源域数据完成对无标签目标域的故障分类,该方法采用一维卷积神经网络作为特征提取器提取原始振动信号的深度特征,并依据源域故障类别设计了一组锚定器以缩小域间同类故障间距并扩大异类故障间距。并且两个轴承故障数据集上的对比试验结果表明所提方法的有效性,实现了高精度的跨域轴承故障诊断的目标,可以作为跨域诊断故障的有效工具。

Rolling bearings as a key part of rotating machinery,seriously restrict the power generation efficiency in wind turbines due to frequent failures.However,traditional rolling bearing fault diagnosis methods require the same distribution of training data and test data,which leads to their insufficient generalization ability and cannot effectively solve the problem of unlabeled cross-domain fault diagnosis in practical industry.Therefore,a domain adaptation bearing fault diagnosis method based on class was proposed,which uses labeled source domain data to achieve fault classification of unlabeled target domain.It uses the one-dimensional convolutional neural network as a feature extractor to extract the depth features of original vibration signals,and according to the class of the source domain to design a group of anchor layers to narrow the cross-domain distance between the same class faults and expand the cross-domain distance between different class faults.Moreover,the comparative experimental results on two bearing fault data sets show the effectiveness of the proposed method,which achieves the goal of high-precision cross-domain bearing fault diagnosis,and can be used as an effective tool for cross-domain fault diagnosis.