Rotating machinery fault diagnosis method based on multi-level fusion framework of multi-sensor information

High-precision fault diagnosis of rotating machinery plays an important role in industrial systems. Today, rotating machinery often has multiple sensors to monitor equipment condition, so it is important to fuse data from multiple rotating machinery sensors for fault diagnosis. Most of the current multi-sensor fusion fault diagnosis methods are single-level, which cannot fully utilize the effective information in multi-sensor data, and have certain limitations. Therefore, this article proposes a multi-level information fusion fault diagnosis method. Specifically, first, multilayer graph data is constructed by analysing the correlation between different sensors as well as samples to realize multi-sensor data level fusion. Then, convolutional neural network and graph convolutional network are used to extract different types of features from the data, and a feature fusion method based on the attention mechanism is proposed to realize feature enhancement. Finally, a decision fusion strategy based on information entropy is established to reduce the impact of misclassification results and maximize the reliability and robustness of the model output. Experimental validation of the proposed method is carried out using the publicly available dataset and pumped storage unit operational state data. The experimental results show that the proposed strategy is of positive significance to improve multi-sensor data fusion fault diagnosis, and the model has a higher diagnostic accuracy compared with other multi-sensor data fusion fault diagnosis models.