Neural Network Coupled Acoustic Emission Sensors for Rock Grinding and Drilling

The problem was to evaluate the on-line detection of rock properties through acoustic emission sensors. Acoustic emission sensors, when attached to crushers, grinders, and roof bolters, are robust, economical, and relatively maintenance free. Our objective was to relate acoustic emissions to rock fracture mechanisms, including micropore closures, linear elastic deformations, and crack propagation. We evaluated acoustic emissions from steel jacketed rock cores during indentation tests. In addition we attached sensors to the rotating shaft of a rock bolter drill. A back propagation neural network "learned" rock fracture characteristics and rock type with 99% classification accuracy. A neural network's calibration and computational abilities suggest better acoustic emission sensor capabilities.