Abstract
In conventional underground mining, scaling is an important process after blasting. The aim is to remove loose rock in a controlled manner. This ensures that no injuries or fatalities occur and also no equipment is damaged by falling rock during subsequent work.
Even today, scaling is often carried out manually by using a scaling bar. The use of suitable machinery, so-called scalers, is also possible. An important prerequisite for successfully performing the scaling process is the detection of loose rock. Commonly, loose rock is identified by an experienced miner based on visual and acoustic investigations. Thus, a significant and complex process is highly dependent on human decisions that might be influenced by physical or psychological factors, which can lead to a suboptimal result. On one hand, the removal of too much material is not desirable from an economic point of view and can create new areas of weakness in the rock mass. On the other hand, if too little material is removed, it is not possible to ensure sufficient safety during subsequent work. Therefore, the use of suitable sensor technologies for loose rock detection can be of enormous advantage.
In the past, the use of long-wave infrared (LWIR) cameras and acoustic methods, such as airborne microphones, have been investigated. If loose rock is present, the conductive heat flow in the rock is interrupted. This enables the use of LWIR cameras for loose rock detection via analyses of the heat gradients. Regarding acoustic methods, if the miner strikes the rock with the scaling bar, loose rock can detected based on the characteristic sound. Thus, an analysis of acoustic frequencies is also viable. However, none of these techniques have been integrated into a scaler.
In this presentation, we will introduce the concept and primary investigations for a multi-sensor-based loose rock detection system which can be integrated into a scaler. This system is composed of a LWIR camera and a structure-borne sound sensor. Since an experienced operator can identify areas with loose rock based on the vibrations of the scaler, the use of structure-borne sound methods could provide valuable information. The information provided by a single sensor is often not sufficient to meet the requirements of a process. One way to deal with this problem is to fuse data provided by various technologies. Therefore, combining the data from different sensors will increase the probability of detecting loose rock. By identifying the signature of the loose rock based on the sensor data, it will be possible to develop a system for supporting the machine operator in identifying loose rock.