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Fachhochschule Dortmund



Condition Monitoring for predictive maintenance adapted to geothermal electric submersible pumps

Fast facts

  • Organizational unit

  • Category

    • Federal project
  • Funding source

    Bundesministerium für Bildung und Forschung - BMBF,Fachhochschule Dortmund

  • Funding program

    BMBF FH Impuls 2016,Eigenanteil FH

  • Duration

About the project

Project description

An essential prerequisite for the expansion of deep geothermal energy, i.e. the use of geothermal energy from the earth's crust at depths of more than 400 m, especially against the background of the planned geothermal and mine energy storage projects for the conversion of the existing district-heating systems in the Ruhr metropolis, is a reliable delivery pumping technique. New technical approaches to increase the efficiency and lifetime of these pumps, as well as prediction systems for pump failures in ongoing operations, are of great interest. Due to the prevailing environmental conditions in which these types of pumps operate, efficiency and lifetime will be reduced, for example by increased wear and deposits. In addition, there are frequent failures of the sensor system, which also lead to a direct reduction in efficiency, as the pumps, for safety reasons, are being operated with a significantly reduced power. Consequently, a scientific study of computer-aided optimization of maintenance intervals as well as an improvement of sensor technology in the field of conveying technology in deep geothermal energy are indispensable.

The objective of the COMPRESS research project is to significantly minimize huge costs due to frequent pump changes and extended plant downtimes. A prerequisite for a minimization is the detection of failure sources, such as material wear, deposits and thermal stress, by monitoring the ongoing pump operation (monitoring) in combination with computer-aided forecasting models for the planning of optimized maintenance intervals (predictive maintenance). For this purpose, it is necessary to describe the relevant operating conditions as well as the wear parts of the implemented downhole pumps and to monitor the operation of the individual pump components by means of sensors or via the evaluation of operating data. The prevailing temperature levels, hydrochemical conditions and drill hole and pump geometries will pose increased challenges to sensors, signal transmission and processing. 

The innovative core of this project is the technical implementation of intelligent pump monitoring linked to a condition-monitoring system utilizing machine learning to make statistical predictions on the condition of a drill-hole pump. This requires both intelligent embedded systems as well as a communication and information technology linked to the central storage of recorded operating data and the implementation of a predictive maintenance system, i.e. the COMPRESS platform. 

Figure 2 shows the UI of the dashboard for visualization of the persisted data, as well as the Predictive-Maintenance System. Each user has its own dashboard, which can be customized by choosing specific pumps and corresponding sensors. Additionally, the condition and predictive-maintenance date are visible in the headline of each pump widget.

Funded by

Federal Ministry of Education and Research (BMBF)



Project partners

  • Fachhochschule Dortmund - University of Applied Sciences and Arts
  • Fraunhofer Research Institution for Energy Infrastructures and Geothermal Systems IEG
  • Westphalian University of Applied Sciences (WH Gelsenkirchen)
  • ProPlus GmbH
Bochum University of Applied Sciences
Westphalian University of Applied Sciences (WH Gelsenkirchen)
Fachhochschule Dortmund - University of Applied Sciences and Arts
ProPlus GmbH

Contact & Team

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