The client’s demands were simple: it wanted to know the amount of material excavated by each shovel at the end of each day to identify ways to improve productivity.
As shovels and excavators are commonly used to excavate ore from the mine pit to be transported for downstream processing, they are the first equipment to handle the material and any changes in productivity can have a domino effect on all downstream processes. Even a small variation in shovel performance can end up costing millions in lost opportunity costs.
In surface mining operations, some of the key factors that affect shovel and truck productivity are:
- Underloading – underutilised capacity means that more trips or passes are needed to transport the same amount of material;
- Overloading – component life is significantly decreased when trucks are loaded beyond their rated capacity. This can lead to premature tyre wear and suspension failures; and
- Consistency – shovel productivity can vary dramatically between different operators and loading practices. By not carefully monitoring shovel performance, unidentified inefficiencies could be costing millions in lost productivity.
Many operations use on-board truck scales that are installed by the truck manufacturers. These systems rely on the strut pressures to determine the payload of the material in the truck. However, this approach can be quite costly as every haul truck must be retrofitted with a payload system; a large mine might have 50-100 trucks.
Another drawback of this approach is that it can be challenging to keep the systems calibrated across a large fleet of trucks. If the on-board scales are not routinely recalibrated, there may be noticeable variations in payload measurements from one truck to another, making the measurements unreliable.
The ShovelMetrics system by Motion Metrics is a shovel-based payload monitoring solution that senses the payload of every bucket as the shovel is loading. The benefit of this approach is that the shovel operator can determine if a load will overload or underload a truck before dumping to the truck.
In addition, as a typical mining operation will have 5-10 trucks for every shovel, the overall cost of installing and maintaining a shovel-based payload system will probably be less than an on-board truck scale system. Due to the maintenance and cost benefits, the client decided to go with the ShovelMetrics Payload Monitoring System, which was installed on its fleet of Komatsu PC8000 mining shovels.
The next challenge was to get the payload information from the shovel to the mine management. As the colliery already had robust wireless infrastructure in place, establishing connectivity between the shovels and the management offices was relatively straightforward.
To be able to automatically process this information and generate daily productivity reports, the client uses Motion Metrics’ centralised equipment-management system, MetricsManager. MetricsManager automatically consolidates all the equipment information in a centralised data server and makes the information easily accessible to anyone on the mine network. Its web-based interface enables users to analyse the data and generate customised reports.
By using these powerful analytic and visualisation tools, inefficiencies and inconsistencies can be easily identified by comparing the performance of different operators, machines, locations and mine sites.
Motion Metrics worked directly with the client to design a daily report according to the client’s specification. The end product is a simple two-page report that provides key performance indicators for each shovel with shift breakdowns. The key performance indicators include tonnage information, time usage breakdowns and cycle statistics. At the start of each day, the mine management automatically receives an email containing the productivity reports on each shovel, which they can use to quickly identify if productivity is not up to par.
Mining operations have become increasingly complex with the introduction of new systems designed to improve safety and productivity. The amount of information provided by these systems can be overwhelming if they are not managed properly. Motion Metrics states that the MetricsManager suite of tools and applications can ensure that important notifications go to the right person at the right time, and it offers the flexibility to be integrated with the client's current workflow, helping to keep users connected.
In addition to payload monitoring, the ShovelMetrics system can also provide missing-tooth detection, tooth wear monitoring, rock-fragmentation analysis, proximity detection and surveillance monitoring, in the same system, using a single display. This unified approach simplifies maintenance and reduces operator training, allowing a mine to focus on its day-to-day operations instead.
MetricsManager is compatible with these additional features and provides an interface to analyse incidents and data. A dashboard provides a quick status summary of all the connected equipment and indicates if any equipment needs attention. In addition, customised alerts can be configured to notify the appropriate personnel, when incidents occur or productivity benchmarks are not met.
The new MetricsManager Mobile App for Android devices allows users to receive immediate notifications for critical events and review equipment productivity statistics on-the-go. The simple and intuitive application ensures that critical alerts are received and that appropriate action can be taken even when you are away from your desk.
In addition, the mobile app allows MetricsGear-compatible smartwatches, such as the Moto 360, the Sony Smartwatch 3 and the LG G Watch, to be connected so that critical events are received immediately. With a quick glance at the smartwatch, a summary of a critical event is provided to ensure that important notifications, such as when a shovel has lost a tooth, are never missed and unnecessary downtime is prevented.
Motion Metrics specialises in developing advanced monitoring solutions designed to improve safety, efficiency and productivity in mining. See: www.motionmetrics.com