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Brownfield Demonstrator

University Osnabrück - Campus Lingen

In many machine parks you will find machines and systems from different epochs of time. You would like to keep the mix of modern high-performance machines and old machines that still work perfectly. Nevertheless, you would like to monitor both old and new machines in the same system.


The University of Applied Sciences Osnabrück at their campus in Lingen demonstrates the application using a table drill.


Representative for all sizes of metalworking machines, this project stands for the possibilities that arise for brownfield machines regardless of their age.


The job for RSConnect is: Retrofit with local evaluation of the data extracted.


This means monitoring the electrical energy (active, apparent and reactive power) as well as the voltage / current and energy consumption.

In addition, environmental data such as temperature, pressure and humidity were recorded.

A vibration sensor was used for the machine status itself, which detects the vibration and the speeds of the x, y and z axes.


For the access, a WLAN-AP is being used. A permanently installed screen like at the university can also be expanded with a mobile device. Integration into your company network is possible so that you have access from anywhere.


The data can now be read out with a web interface. The control processes the data recorded by the sensors in the background so that the desired calculations, e.g. of the OEE's (Overall Equipment Effectiveness) or the quality factor, are displayed.

The worker at the machine has the option of using action buttons to describe the process or the reason for the disruption in more detail, for example during a set-up process.


The vibration sensor enables us to deduct the exact condition of the machine. A fast-moving drill vibrates differently than a slow-moving drill. The system also indicates broken bits and - with sufficient reference data - can also warn in advance if the drill is working towards the end of its life. Predictive maintenance is the keyword here.


A runtime detection, i.e. the recording of individual work processes, reflects the duration per workpiece. In the case of series production, the required machine time per piece can be determined.

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