Rotating Machine Systems Fault Diagnosis Using Semisupervised Conditional Random Field-Based Graph Attention Network

Data-driven intelligent diagnosis methods require sufficient labeled data during training, which are usually limited in practice. A semisupervised conditional random field-based graph attention network (CRF-GAT) algorithm is proposed in this article for fault diagnosis and condition monitoring. The proposed method combines the advantages of CRF and GAT, and therefore, it achieves semisupervised fault diagnosis by modeling the label dependency and learning object representations. The scheme is optimized with the variational expectation-maximization (EM) algorithm. Specially, the clustering with adaptive neighbor (CAN) method is introduced for constructing the graph. The proposed method is applied in induction motor (IM) and permanent magnet synchronous motor (PMSM), which achieves the identification of the motor status, fault severity, and working condition. The results show that the CRF-GAT can realize an accuracy of above 97% with even below 10% of labeled samples for training, which demonstrates that it is an effective method in semisupervised fault diagnosis.

Automated summary

A semisupervised conditional random field-based graph attention network algorithm is proposed for fault diagnosis and condition monitoring, achieving semisupervised fault diagnosis by modeling label dependency and learning object representations, and optimized with the variational expectation-maximization algorithm.