In many machine parks, you will find machines and systems from different eras. You would like to retain the mix of modern high-performance machines and old favorites that still work perfectly. Nevertheless, you would also like to have the monitoring features of modern machines on the old equipment.
Osnabrück University of Applied Sciences at the Lingen campus demonstrates the use case using a bench-top drilling machine.
Representative of every size of metalworking machine, this project epitomizes the possibilities that arise for brownfield machines, regardless of their age.
The request for RSConnect is: Retrofit with local evaluation of the data obtained. This means monitoring the electrical energy (active, apparent and reactive power) as well as the voltage/current and energy consumption.
Furthermore, environmental data such as temperature, pressure and humidity were recorded. A vibration sensor was used for the machine status itself, which recognizes the vibration and the speeds of the x, y and z axes.
Access is via a WLAN access point. A permanently installed screen such as at the university can therefore also be supplemented by a mobile end device. Integration into your company network is possible so that you have access from anywhere.
The data can now be read out via a web interface. The control system processes the data recorded by the sensors in the background so that the desired calculations, e.g. the OEE (Overall Equipment Effectiveness) or the quality factor, are displayed.
The operator at the machine has the option of using action buttons to describe the process or the reason for the fault in more detail, for example during a set-up process.
The vibration sensor enables us to derive the exact status of the machine. A fast-running drill vibrates differently to a slow-running drill. The system also indicates drill breakages and - with sufficient reference material - can also give a predictive warning when the drill is approaching the end of its service life. Predictive maintenance is the keyword here.
Runtime detection, i.e. the recording of individual work processes, shows the duration per work piece. In series production, the required machine time per piece can be determined in this way.