About MAMMA – Projekt objectives and strategy

Unexpected and unplanned disturbances and malfunctions of machines and infrastructures are main causes for operational breakdown in underground mines. The impact on operating time, cost, efficiency and productivity of the mine is tremendous.

In this project the availability, efficiency and the safety of the machines and mine plant is improved using a smart, integrated and holistic maintenance system. In particular under the aspect of autonomous mining the requirement of proper working machines as well as good condition of the mine is growing up also to the fact that less staff is at the mine.


Figure 1 illustrate the complexity of a mine with several different kinds of machines and surveillance tasks at the surface and underground.

Based on the situation with all the different kind of information, a strong need of data handling tools for gathering, aggregate and review are necessary. This data will visualize the condition of the total mine to manage the service work. The results will be a reduction of incidents, an increasing availability of the mine production and will enable competitive mining operations also in challenging environments for providing a sustainable supply of raw materials.

Figure 2 illustrated the data flow of the planed product. The steps from big data to smart data leads to the decision for inspection or service action. This will done for the all machines and the mine. So their dependencies pointed out and could be handled to improve the production.

To enable competitive mining operations in future, these improved strategies and technologies are desperately required. As the cost pressure is getting higher, it is essential to streamline the mining operations, to be sure that the operations can be operated cash flow positive also in an environment of suppressed commodity prices. One step to reach this is to optimize the costs of maintenance. This requires a multistep operational approach embedding from the one hand an optimal management of work order from a database based planning module and on the other hand optimizing the costly maintenance intervals. Both issues finally will have a huge impact on cost reduction integrating information technology (IT) based system elements into the mining machines.

This will allow automatically generated status messages of the mining machines, collection and further processing of data obtained from the mining machines, wireless communication of mining machine data by using sensor nodes. With regard to the foregoing mentioned two aspects, this project focus on the second aspect, keeping the mining machine longer in service during its utilization and also adapting on new functions required, to expand the mining machine life cycle.

This requires integrating the respective system elements such as sensor nodes, wireless communication access points, mining machine dashboard to organize and present information in a way easy to read, into the mining machine for automatically machine data collection and processing keeping the mining machines longer costeffective in service. The maintenance mining machine life cycle management can also be used as a platform enhancing mining machine functionality in case of critical operations for which high levels of confidence and robustness is required.

The objective of this project is to establish a virtual view of the monitored mine which provides both real-time and historic data on-demand.

The goal is then to develop new applications which build upon this data and provide a holistic approach to monitoring and analysis as a whole, i.e., monitoring the machines, equipment and also operations. Both real-time on-line monitoring and “post-mortem” analysis after an event will be possible. The collection and maintenance of historic data is of strategic importance, since it permits the evidence based verification of hypothesis with respect to cause and effect of events and with this permit the prediction of future events.

Such a monitoring system will also support the process of continual improvement in the mining operations by enabling the identification of sub-optimal behaviour and possible damaging procedures.

Understanding machinery.