Here you will find a selection of recently completed research projects, structured according to the Smart Services of the data value chain, which you can also find in our research overview.
The aim of the CDIO II project was to raise awareness of the principles of good, contemporary teaching in modern raw materials education. CIDO, an international framework program with 12 standards, stands for Conceive Design-Implement-Operate and thus for what engineers should be able to do after their training, namely to solve and overcome problems and challenges in a real, complex, industrial and international environment.
The project was the first to apply the principles to European raw materials education. Faculty Development courses took place at the participating universities, joint project courses with industry participation were offered, a worldwide overview of the use of laboratories in the training of mining engineers was developed, and guidelines for innovative laboratories as learning environments were developed.
POSITIONING & NAVIGATION
The project UNDROMEDA (Underground Robotic System for Monitoring, Evaluation and Detection Applications) contributed to the development of a robotic underground measurement system for autonomous 3D mapping and monitoring. The system is based on a mobile, wheel-driven platform, which is additionally equipped with a flying drone, in order to access particularly unknown, inaccessible or dangerous areas in underground mines and other underground environments, such as tunnels or canal systems. UNDROMEDA was a milestone project in the current development of the springboard for the "invisible, pollutant-free, intelligent, safe and fully autonomous" mine and will enable us to meet the associated challenges for future mining in terms of social and ecological acceptance and economic efficiency.
UPNS 4D+ stands for the development of an underground 4D+ positioning, navigation and mapping system for the highly selective, efficient and highly secure extraction of economically strategic raw materials. The aim of this research project was to enable the highly selective, efficient and extremely safe extraction of mineral raw materials. The main focus was on the extraction of rare earths from "domestic" existing deposits and the use of the system for the exploration of new deposits. Within the framework of the project, an underground deposit positioning, navigation and mapping system in the form of a mobile autonomous and intelligent robot system was successfully developed for the first time.
As part of the Real Time Mining project, a real-time system was developed over 48 months as part of a Horizon2020 funded project, which enables process control of the entire mining process. The focus was on the collection of material, location and machine information during the extraction process in order to sequentially optimize the deposit model developed from the exploration. With the help of this information, it is possible to adjust the long-term and short-term planning of the extraction in real time.
As part of the project Cutting Drum 4.0, steps have been taken towards developing an intelligent cutting drum that is equipped with sensors suitable for mining, so that, for the first time, material recognition can be realized directly during the cutting process. By distinguishing between coal and secondary rock during the cutting process, more efficient extraction can become possible. As part of the development of such a cutting drum, the relationships between different material classes and the AE signals had to be identified and evaluated right at the cutting tool.
As part of the Blue Nodules project, the Institute for Advanced Mining Technologies (AMT) developed a concept for characterizing the material flow using acoustic emission technology in deep sea environments. Acoustic emission sensors have so far been used, for example, in the condition monitoring of pressure vessels and bridges. In the first preliminary tests at the AMT Institute, the physical phenomenon of acoustic emissions was used to characterize the material flow of bulk material. The AE signals recorded during transport and impact processes are evaluated with regard to characteristic parameters. Characteristic values are calculated and compared with already recorded characteristic values from previous reference measurements. The aim here is to determine differences and thus characteristic parameters for different materials. This concept has now been adapted for use in the deep sea to characterize the material flow of manganese nodules and overburden. The results will be used to determine the process efficiency and, if necessary, to control the process.
The goal of the project OMMA is the development of an online measuring system for material flow characterization in processing plants of the gypsum industry. The background for the need of such a system is the necessary quality assurance of the products in companies of the primary raw material industry. A precise knowledge of the composition of the raw materials to be processed is crucial. The aim of the OMMA project is therefore to implement a real-time measurement system integrated into the preparation process for inline characterization of the material flows.
As part of the project "Online Analysis Methods for the Extraction of Mineral Resources" (OFUR) funded by InnoNet (Promotion of Innovative Networks), the IMR, in cooperation with several small and medium-sized companies and the Fraunhofer Institute for Laser Technology (ILT), developed a system for real-time elemental analysis of raw materials during the extraction process.
The goal of the project MaMMa (Maintained Mine & Machine) is to improve the availability, efficiency and safety of machines and mines through the use of an intelligent, integrated and holistic maintenance system. Unexpected and unplanned machine and infrastructure failures are the main cause of costly underground failures and are to be minimized by the software system developed in the MaMMa project. At the same time, the system enables employees and consultants to better and more efficiently plan maintenance work based on real-time data on the condition of machines and equipment underground.
The aim of the project SIMS (Sustainable Intelligent Mining System) was to sustainably improve mine safety through a higher degree of digitisation, automation and robotics, to reduce the environmental impact of mining and to increase the overall efficiency of mining operations. In addition, SIMS aimed to identify direct and measurable factors influencing sustainable mining and to sensitise the wider population to the need for mining.
The I²Mine project was an extensive project for the development of innovative technologies and concepts for the design of the intelligent underground mine of the future. The project comprised different components, from the development of software and hardware for improved rock control and mining technology to new digital recording systems and management tools to improvements in occupational health and safety. The AMT (then IMR) developed, among other things, a method for boundary layer detection to enable a more mechanised mining process.
Today, concrete spraying processes in mining and tunnelling are still carried out manually. The shotcrete application and the quality control are subject to the subjective impression of the operator. A sufficient examination of the quality of the working process cannot be guaranteed in this way. In order to support the operator and increase the success of the process as well as the quality control, GTA Maschinensysteme GmbH together with the Institute for Advanced Mining Technologies of the RWTH Aachen University developed a novel concrete spraying vehicle equipped with various sensors. This made it possible to record the tunnel profile, monitor the application of the shotcrete slab and carry out a final, data-supported quality control. In this project, AMT was responsible in particular for the development of the localisation technology consisting of Ultra-Wide Band (UWB) and INS, including skilful sensor data fusion. In addition, the laser scanner technology and the associated software were developed at AMT. Special attention was paid to the algorithms and time synchronization of the UWB modules.
The aim of the project "The intelligent telescopic boom for extreme operating conditions" was to develop a robust, self-monitoring and 360 degree endlessly rotatable telescopic boom for adverse environmental conditions in the extractive and related industries. The new development is intended for underground use in gypsum-anhydrite mines around the world for depletion of roadways and mining sites.
The telescopic boom enables the use of new, particularly efficient tools such as the "Xcentric® Ripper", which requires special design consideration due to its high-frequency mechanism. At the same time, the radius of action of the telescopic boom is increased by extending the telescopic stroke.
The research project "Observer-based Condition Monitoring System for Main Transmissions in Wind Turbines (BCMS)" was carried out within the framework of the 6th Energy Research Programme of the Federal Government with the focus on an environmentally friendly, reliable and affordable energy supply.
The research project dealt with the development of a novel, integrable condition monitoring and prognosis system for main gearboxes of wind turbines. In order to achieve sufficient reliability in the prediction of component damage and failure, the observer-based methodology was used, in which data from simulations running parallel to the measurement are used to map a fault-free WTG system. Deviations of the measured, real plant behaviour compared to the simulation are used for fault detection and condition prognosis. Thus, the accuracy and the scope of information of condition monitoring systems on WTG main gearboxes could be increased and an automated and reliable damage reporting system could be made possible.
The aim of the SiZu research project was to develop an integrated forecasting and analysis tool for assessing machine conditions. For this purpose, the AMT (at the time IMR) for the first time combined condition monitoring and real-time simulation in a single system and made them jointly evaluable.
The System Condition Analyser provides a comprehensive and meaningful database, on the basis of which the project team was able to develop new maintenance strategies. With the development of the Condition Analyser, the AMT pursued two core objectives:
- The development of a condition forecast oriented maintenance strategy
- The Development of an Automated Failure Cause Analysis
The aim of the project was to develop an integrated simulation and multisensor monitoring system for the dynamic design and condition diagnosis of wind turbines and a comprehensive consideration of the complex interactions between the subcomponents of dynamically highly loaded wind turbines.
The aim of this project was to develop an electromechanical simulation model that simulates all components of a large belt system from the drive motor to the conveyor belt in one simulation model and thus makes the interactions between the individual components calculable.
The aim of the research project was to improve the condition monitoring of rotating and oscillating rolling bearings using acoustic emission technology. By early recognition of a developing damage suitable maintenance measures should be carried out promptly or larger repairs should be planned according to demand. The direct and indirect maintenance costs saved as a result increase the economic efficiency of the plant.
The aim of the SESI research project was to enable manufacturing companies and industrial service providers to improve the reliability of plants by forecasting maintenance requirements and times, to increase availability and to minimize direct and indirect maintenance costs by means of demand-oriented maintenance and thus to increase their competitiveness.
Unplanned shutdowns or sudden plant failures in mining plants quickly cause very high costs, which must be avoided. Although the measuring systems used for condition monitoring cover a wide range of possible applications, they reach their limits in special areas. The i-MaSS research project addresses exactly this problem. An interdisciplinary consortium from the fields of raw material technology, mechanical engineering and electrical engineering developed a cost-effective, miniaturized, adaptive and self-sufficient measuring system for improved condition monitoring.
PERSON- AND OBJECT RECOGNITION
The project INESI (Increasing Efficiency and Safety Improvement in Underground Mining Transportation Routes) has three project objectives, in which sensor methods developed at AMT are used:
- to increase safety in underground mines by using a precise Ultra-Broadband Radio Localisation System (UWB)
- to increase efficiency by determining the position of monorails and the resulting optimisation of transport logistics using ultra wideband radio technology.
- to enable the detection of persons on belt conveyors in hazardous areas by means of infrared thermography (IR)
As part of the EU-funded FEATureFACE project, scientists at the IMR (AMT's predecessor institute) combined the strengths of several technologies to develop a multi-technology collision avoidance system: the world's first fail-safe safety system.