In practical engineering applications, the working load of the rotor system keeps changing, and its working environment is severely polluted by noise, leading to a decline in the performance of traditional fault diagnosis methods. To better extract fault features in a noisy environment and improve the diagnostic accuracy, a fault diagnosis model based on the Visibility Graph algorithm and Graph Transformer is proposed.
This paper proposes a fault diagnosis model based on Visibility Graph algorithm and Graph Transformer. Firstly, the spectrum of the original vibration signal is constructed into a Visibility Graph, with the spectrum mean value as the node value and the visibility relationship between nodes defined. Subsequently, the graph transformer is used to extract the global node features and topological structure information in the graph, and the long-range dependencies are captured through the self-attention mechanism. Finally, the fully connected layer and the Softmax classifier are combined to achieve end-to-end fault diagnosis.
Experimental results on the MAFAULDA dataset demonstrate that the proposed model achieves a diagnostic accuracy of 99.91% under noise-free conditions, surpassing comparative methods by 1.12–14.53%. Under −10 dB Gaussian white noise interference, the model maintains an accuracy of 74.36%, outperforming baseline models by 1.42–48.49%. Further cross-scenario verification on the CWRU and XJTU-SY datasets shows that this method has strong robustness against compound faults and extreme noise. The results show that this method effectively enhances the ability to extract fault features in noisy environments, providing an efficient and reliable solution for intelligent monitoring of rotor systems.
In order to effectively realize end-to-end intelligent fault diagnosis, enhance the robustness and anti-noise performance of the model, a new fault diagnosis method of the rotor system based on the Visibility Graph algorithm and Graph Transformer is proposed in this paper. By constructing the vibration signal spectrum as a viewable data and then effectively capturing the topological relationship of the graph in the global range through the self-attention mechanism, the fault diagnosis is realized in the form of node classification. Finally, the fault analysis and classification of the vibration signals of the rotor system under the background of no noise and noise are carried out.
