Industrial plants must function effectively. Remedying production downtimes and breakdowns is expensive and time consuming.
That is why companies collect data to evaluate how their facilities are doing. Operators can analyse these huge amounts of data and use it as an early warning system when problems threaten. This will keep their facilities running more reliably and save energy.
Large amounts of data are produced when industrial companies monitor their facilities. Sensors check temperature, pressure, power, or energy use data.
“If you’re scanning to the nearest second, it’s easy to rack up several terabytes of information in under a week,” says Dr. Olaf Sauer from the automation business unit at the IOSB.
But often there is a lack of suitable methods to evaluate the information. “Today’s operators use only about seven percent of this data for maintenance or protection from breakdowns,” adds Sauer.
The researchers use smart data mining methods to calculate the optimum operational steps for each production process. This reference model is then compared to data from current operations in order to quickly identify and precisely locate any discrepancies before thoroughly eliminating them.
There is no need for detailed separate modeling of complex plant structures. Special data storage systems record the information in real time and send it over the network to a database.
Next, software normalises the data, makes it comparable, and establishes relationships. The results are then presented in a clear way – for instance in the form of a three-dimensional map.
“Mountains” and “valleys” depict the individual process phases; any disruptions or anomalies can be quickly identified.
The engineers also use it to monitor and analyse facilities’ energy demand. This information makes it possible not only to judge the state of the facilities but also to reduce their power consumption by way of appropriate adjustments to the controls.
“Condition monitoring” is the name production experts give this use of modern ICT systems to monitor industrial facilities so as to prevent breakdowns.
Most industrial companies today use technology of this sort, but in many cases they monitor only individual components and not the entire facility – even though that is what they should be doing.
This is especially true for continuous manufacturing processes, where creeping change can suddenly cause a breakdown unless operators have their eye on all the variables. One example could be a pipeline blockage as a result of a gradual build-up of liquid or viscous material deposits on the pipe’s inner walls.
It is also rare for people to work up a reference model directly from collected data in the way the IOSB tools do. An additional challenge is that today’s monitoring systems generally run on a standalone basis and are not part of the production ICT system.
But industry has recognised that it needs to catch up, and there is clearly a trend toward integrating them into manufacturing execution systems (MES).
"We’re still a long way away from the vision of Industry 4.0, in which smart machines automatically report of their own accord when they need maintenance or spare parts. But our methods bring us one step closer to reach that goal,” says Sauer.
[More information: Fraunhofer Institute for Optronics, System Technologies, and Image Exploitation IOSB]