Multi-source rolling bearing fault diagnosis under variable working conditions based on GCN
Rolling bearing is a key component of rotating machinery,and its health status identification is very important. Transfer learning is widely used as an effective tool in the field of fault diagnosis,but the single-source migration learning method may lead to poor generalization performance or even cause negative migration,resulting in poor recognition results. In this paper,a multi-source transfer learning method (MS-GCN) based on multireceptive field graph convolutional network (GCN) was proposed. By learning transfer knowledge on multiple source domain data,the fault diagnosis of rolling bearings under variable working conditions was realized. Firstly,the vibration data samples were converted into two-dimensional time-frequency diagram samples by wavelet transform. N sets of source domain samples and target domain samples were constructed to obtain N sets of source domain-target domain sample data pairs. Secondly,the deep convolution network was used to learn the high-dimensional features of each set of data pairs. Then,the data structure of the proposed features was learned by the multireceptive field graph convolution network,so that the adaptive method can fully learn the domain invariant features,align the source domain and the target domain features more effectively,and train N groups of classifiers. Finally,the average value of the classification results of N groups of classifiers was taken as the state recognition result of the target domain samples. Based on the bearing data set of Jiangnan University,the proposed method is experimental validation,and the classification accuracy of the proposed method for the four different states (normal,inner failure,outer failure and rolling element failure) are above 99％ in three different sets of variable operating conditions bearing fault diagnosis tasks,which improves the diagnostic accuracy by 0.22～8.27 percentage points compared with other methods. The comparison results show that the proposed method for the identification of faults in rolling bearings under variable operating conditions can effectively diagnose the type of bearing faults,which has a certain engineering practical value.
fault diagnosismultireceptive field graph convolutional networkmulti-source transfer learningdeep convolutional networkrolling bearing
万方数据
维普
