Continuous Monitoring of Mechanised Breaker Line Supports to Investigate Roof and Pillar Behaviour (11th International Conference on Ground Control in Mining)

A breaker line support (BLS) monitoring system, BLSmon, has been designed and constructed by the CSIRO and installed and commissioned at Laleham No.] Colliery, Queensland. This system was designed to record hydraulic leg pressure and canopy position measurements and relay these, in real time, to the surface. The system has shown itself to be capable of operating on the BLSs without any adverse affect on production. Analysis of the results of the monitoring exercise has shown that rate of change of leg pressures on the BLSs could be used as an aid in the prediction of adverse mining conditions. Under favourable mining conditions the supports were observed to initially unload from the set pressure, this unloading reduced with time and by the end of a typical lift cycle had either ceased or reversed slightly. This behaviour was considered to be due to one of four possible causes, these being; the compaction of floor debris, and/or compaction of the floor, and/or crushing of the roof and /or support creep. The actual cause can only be determined through detailed monitoring of both roof and support convergence. However observational data would suggest that compaction of debris and softened floor material beneath the supports was the most likely cause. The few positive loading rates recorded under these conditions were of short duration and occurred towards the end of individual lifts. Where mining conditions were unfavourable the negative rates of change of pressure, unloading of the support, were reduced throughout a lift, in both duration and magnitude, when compared with favourable mining conditions. Over the same period the positive rates of change were observed to be greater. The rate of change of loading and convergence can be utilised to identify the onset of instability in the lift area. However, before this facility can be utilised by the mine for the reliable prediction of instability more monitored data and sophisticated processing techniques are required.