Acoustic emission-based intelligent identification and dual early warning for coal fatigue failure under multistage cyclic loading.

To address the limitations of insufficient accuracy and timeliness in the early warning of coal instability under multistage cyclic loading, this paper proposes an intelligent recognition and dual early warning method for coal damage based on acoustic emission parameters. The multistage cyclic compression test of coal is performed to obtain five acoustic emission parameters, and the average event intensity (ERR) is used to characterize the sudden changes in coal energy release. The Sparrow Search Algorithm (SSA) is adopted to optimize the hybrid model of the Transformer and Gated Recurrent Unit (GRU) to achieve high-precision recognition of coal damage stages. On this basis, the early warning coefficient (EW) is calculated by using the Isolation Forest algorithm and the CRITIC-TODIM method, and a dual early warning system of "preliminary prompt with ERR and comprehensive confirmation with EW" is established. The results show that the recognition accuracy of the optimized model for the fourth damage stage is 11.7% higher than that of the SSA-GRU model. The early warning lead time of ERR is between 60.5 and 185.7 s, and that of EW is between 10.5 and 101.5 s. The proposed method integrates physical mechanisms and data-driven technology, effectively improving the accuracy and reliability of coal damage early warning under multistage cyclic loading and providing theoretical support and technical reference for the early warning of dynamic disasters in deep coal mines.