Abstract 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.
