Fault Prediction for Electromechanical Equipment Based on Spatial-Temporal Graph Information

Fault prediction of electromechanical equipment can greatly reduce its maintenance cost and prevent catastrophic damage. In order to realize the accurate fault prediction of electromechanical equipment, a fault prediction method based on spatial-temporal graph information is proposed in this article. In the proposed method, the signal data of each intermittent monitoring period are expressed by Markov field graph information, and the spatial-temporal correlation features of graph information are extracted and studied by multivariate spatial-temporal graph neural networks. The effectiveness of Markov graph information for different state expressions is demonstrated by motor fault data from the motor fault experimental platform, and the bearing fault prognostics data is used to demonstrate the feasibility and accuracy of the proposed fault prediction method. The results show that based on the local spatial state information and global time correlation information of monitoring signals, this method could accomplish accurate long-term and short-term fault prediction tasks, respectively.