Business Development Manager
Draglines are massive pieces of equipment used in strip-mining operations to remove overburden above coal. They are incredibly valuable assets that require strict maintenance schedules and demand careful monitoring for safe and efficient functioning.
Traditional methods of monitoring their conditions are far from efficient and often require shut down of production for testing of critical components, careful measuring, then analysis and reporting for diagnosis of any potential issues. The entire process can be time-consuming and incredibly costly, especially when an asset is out of service.
We recently partnered with a major mining operator in North America to implement a condition-based maintenance scheme for a Dragline based on real-time component operating data.
Based on its current practices, the operator did not know the current condition of key components, was unable to predict failures, carried out corrective maintenance that was purely reactive and as a result, experienced costly delays due to unplanned asset downtime.
Having already placed a number of sensors on key components within the rotary sub-assemblies of the dragline to collect vibration data for analysis, they were interested in analysing and reviewing the data collected in an effort to detect component wear before catastrophic failures occurred and to receive the information in near real-time.
With historical data and recently collected vibrations data from the draglines, analysts using BMT DEEP were able to spot anomalies from established vibration signatures which indicated declining health in certain components, and were swiftly able to pinpoint the exact moment a sudden failure occurred and record the variations in amplitude, frequency and intensity that led to the failure.
This intelligence, coupled with AI and machine learning, equips mining operations and analysts with the important insights needed to better understand the condition of their equipment, establish predictive maintenance programs, minimise unplanned maintenance and downtime, and ultimately saving the operation considerable costs.