Virtual Measurement of Explosion-Proof Performance: Application of an Improved RBF-GMSE-Based Surrogate Model to the Safety Performance Characterization of Coal Mine Equipment

Explosion-proof safety evaluation is critical for coal mine equipment operating in hazardous environments. Traditional methods rely on physical explosion testing, which is time-consuming, costly, and impractical for large-scale or complex systems. We propose a real-time virtual measurement method based on an improved combined surrogate model to address these limitations. A digital twin framework is constructed by integrating internal explosion transmission data with physical models of gas deflagration and enclosure impact mechanics. A transient multi-physical reduced-order model is developed using Latin hypercube sampling and machine learning. The core prediction model, RBF-GMSE, combines a radial basis function surrogate model and a generalized mean square error model through adaptive weighting. This model is trained on dimension-reduced finite element data and used to predict explosion-induced stress, strain, and displacement in real time. A virtual measurement system is implemented using this framework, enabling accurate, dynamic safety evaluation of explosion-proof equipment. Validation against simulation data shows a maximum prediction error below 1.89% and an average correlation coefficient of 0.9779, confirming the model’s high accuracy and robustness. This approach offers an intelligent solution for efficient and precise acquisition of explosion-proof safety characteristics in coal mine equipment.