The motivation for this project is the unquestionable need to improve the performance and overall efficiency of plant and machinery used in conjunction with mining and the handling of raw materials. It is inspired by the positive results obtained from a number of projects performed by DMT with respect to machine maintenance and failure prediction.
This has includes incident analysis, whereby the cause and consequences of specific incidents can be analysed in realtime historical data, if it is collected in a systematic manner. Additionally issues such as commissioning support to shorten the time required to commission large pieces of equipment, automatic operations recognition to identify possible improvements in machine operations. The Clausthal University of Technology has invested considerable effort into the use of IoT and cloud methods into the mining environment.
Additionally the Chain of Automation has, in cooperation with industry, developed prototype to extend the use of monitoring far beyond just maintenance.
It is a very promising prospect to use IoT and cloud methods to simplify the collection of real-time machine and measurement data in such environments. A wider scale of data collection will permit additional analysis techniques and with this completely new applications for the monitoring data. This will improve the performance and added value of the current maintenance methods and extend the applications into fields previously not considered.
The IoT techniques together with cloud services and a data ingestion process optimized for multichannel real-time sensor and actor data will enable “data as a service” providing an “on-demand” access to both on-line and historic data from the monitored mine and associated systems. This will greatly simplify the implementation of applications, since the experts with domain specific knowledge can focus their effort on embedding the a-prior knowledge into the application, rather than having to manage data.
In the environment of the existing projects the proposal participants were requested by several customers, belonging to the international mining industry, for an improved solution to maintain their heterogenous machinery and the mine itself.
One of these projects for example is the maintenance planning system of the DMT which has been in operation for nearly 6 years on a coal mine of a key customer. A TRL of 6 has been reached.
Increasing cost pressure in the extraction of raw materials leads to the need to optimize the running time of the machines used. A step in this direction is to minimize downtime through maintenance and preventive maintenance by adapting the maintenance intervals to the actual running time and load of the individual machines. For this purpose, it is necessary to identify suitable measured indicators as well as to develop algorithms by means of which the running time and load of a machine can be reliably determined. From the data obtained in this way dynamic maintenance intervals can be determined in turn.
At the same time, such a tool can be used for the feedback of performed maintenance so that a timely overview of the maintenance state of the machinery is obtained.
The data acquired also means that it is possible to determine the degree of maintenance which is summarized in a key figure. With the help of this figure it is possible to recognize whether the required maintenance of a machine has actually been carried out within predetermined waiting times. This measure can thus be used as a measure of the efficiency of the maintenance. The optimum value is one hundred percent. An undercut of the optimum value indicates a too low maintenance activity, whereas an overshoot indicates that too much time is spent on the maintenance work.
The maintenance planning system, developed in cooperation between a customer of the mining industrie and DMT, was developed on the basis of findings obtained within the framework of a research project dealing with the use of machine diagnostics and process data for the maintenance control of face conveyors.
The system consists of the modules data collector, data compressor, diagram service, SAP-IO, web interface and windows-based administration interface. The first four modules are designed as a service and form the basis of the system. The maintenance staff accesses the system via the web interface and can in this way catch up on upcoming maintenance dates and can also send a feedback of performed maintenance. The configuration of the system as well as the adaptation of the evaluation algorithms for the determination of the dynamic maintenance intervals are carried out via the administration interface.
Within the project the following extensions are to be made: