In terms of safety and economic efficiency, the automation of underground extraction machinery is one of the most significant goals in mining. One major challenge in rock cutting is to adapt process parameters to variable rock conditions. The guidance of such extraction machines today is still dependent on an experienced machine operator who particularly listens to rock cracking. Those human perceptible emissions can also be gathered by acoustic sensors and linked to rock properties.
As part of the European-funded H2020 research project Real-Time-Mining, different acoustic sensors technologies were tested to build an acoustic fingerprint of rock cutting processes. Test campaigns in a laboratory environment as well as in the field have shown that vibrations and Acoustic Emissions (AE) can be linked to the energy demand of cutting machinery. In addition, the analysis of AE signals allows to depict different rock breakage behaviour within cutting different types of rock.
Furthermore, the tests performed at the RWTH RockCutting Center were able to demonstrate that the use of AE sensors could lead to a real-time notification of crossing the boundary between coal and host rock. This could possibly support machine operators to adjust machine parameters, e.g. lowering the cutting drum. The data evaluation is still ongoing and indicates that further information about the cutting process could also be delineated from additional analysis of the vibration signals.