Health status identification of scraper conveyer based on fusion of multiple graph structure information

Scraper conveyors are essential coal transportation equipment in underground coal mines, significantly impacting mine production. However, the harsh working environment and long-term use lead to wear and tear, degrading their performance. Therefore, timely monitoring of scraper conveyor’s health status is extremely critical. To address the limitations of traditional methods, which struggle with strong component coupling and require excessive manual intervention, a novel method for identifying health status of scraper conveyors is proposed. This method utilizes a Variational Autoencoder (VAE) co-optimized with Self-Attention (SA) and Normalizing Flow (NF) mechanisms to automatically construct health indicators without supervision, effectively fitting the implicit distribution of the indicators and overcoming the influence of outliers. Additionally, a method fusing multiple graph structure information is introduced, using multiple Graph Attention Networks (GAT) to extract and integrate this information. Experiments with real-world data from the scraper conveyor show that the model’s indentification accuracy achieve up to 98.60% and macro-average F₁ scores up to 96.81%. This approach offers a novel and feasible solution for health status identification of scraper conveyors, with significant practical value.