GEA pilots Industry 4.0 data collection and analysis technology for enhanced efficiencies

02 Apr 2020 --- GEA has initiated piloting of industrial connectivity company Weidmüller's new Automated Machine Learning Software at its Oelde site in Germany. The food and beverage processing supplier is aiming to increase its efficiency and productivity through data collection and analysis and establish new business models. The Weidmüller applications were used in connection with existing IoT scenarios for condition monitoring and the potential for transferring the technology to other areas of GEA has been touted.

Industry 4.0 digital technologies pose great challenges for companies in the mechanical and plant engineering sector but also offer completely new opportunities. Production plants must be individually adaptable to individual products and customer requirements. Kerstin Altenseuer, Head of Service Development at GEA, believes the service business is becoming increasingly interesting.

“We have been dealing with the topic of condition monitoring and condition monitoring of machines and have set up threshold value analyses for quite some time. But we were aware that the potential of this topic is much greater. In the long term, the aim was to map processes or to be able to optimize applications together with our customers. And of course also to establish new business models and areas of application such as leasing or subscription models for our machines,” Altenseuer explains.

Algorithm aptitude
GEA, with over 125 years of expertise in the manufacture of separators and decanters for the separation of liquids, says it has benefited enormously from Industry 4.0 technologies. The company’s plants are used in various industries, including food, chemicals, pharmaceuticals, biotechnology, energy, shipping and environmental technology. With new business models or applications, the company intends to open up new sources of revenue.

“However, we realized relatively quickly that we needed the expertise and help of data experts for this project. “It is not easy to identify and recruit the appropriate experts like data scientists, even if a company like GEA would in principle have good cards. But we would have needed several, which doesn't make things any easier,” continues Altenseuer.GEA is aiming to increase its efficiency and productivity through data collection and analysis.

In its search for a solution, GEA became aware of Weidmüller and the company's expertise in the field of industrial analytics through the “It’s OWL” cluster of excellence. Gea and Weidmüller first worked the project set up and focused on what the central goal would be.

“It quickly became clear that we would first prove the feasibility in a proof of concept and then enable GEA to develop and operate ML models independently,” explains Tobias Gaukstern, Business Unit Head of Industrial Analytics at Weidmüller.

With the help of the Automated Machine Learning Software Service, GEA experts can train machine learning algorithms and statistical models independently. “The AutoML tool simplifies and accelerates the application of ML for application experts, without the expert knowledge in ML being necessary,” Gaukstern adds.

Mechanical engineers often face the problem that their design, automation and process experts cannot easily transfer their knowledge into machine learning solutions. How to bundle this application knowledge in software or even in an algorithm was a significant challenge.

“The solution was very interesting for us because we have many process engineers who know the machines very well and can interpret the data to a certain extent. With Weidmüller’s help we can now transfer this knowledge into an algorithm,” notes Matthias Heinrich, Manager Digital Solutions at GEA. A proof of concept (PoC) with historical data was carried out in Oelde to check how the theoretical considerations can be applied on-site in the production environment at GEA. The aim was to automatically detect anomalies in the behavior of separators in the dairy industry.Long-term, GEA hopes to establish new business models and areas of application such as leasing or subscription models for its machines.

Collaborative advantages
GEA attributes the project’s success to close cooperation between the teams involved. On the one hand, the regional proximity was a great advantage because the project team was able to get together easily and quickly to discuss individual aspects.

“Weidmüller also has a very broad data scientist perspective. At the same time, as a mechanical engineer you feel well understood because you don't just sit down with IT specialists but with engineers who know the machines,” says Altenseuer.

Within the scope of the project, GEA provided the input and the requirements, and Weidmüller then implemented the PoC. “This division of labor has proven to be very effective. We had regular, good coordination and very good results, which formed the basis for the pilot application and finally the transfer served the series," Gaukstern points out.

Further rollout planned
The applications were used in connection with an existing IoT scenario for condition monitoring at GEA. “Everyone is talking about digitization. But in the end, we want to provide added value. We want to take the next step with Weidmüller's solution,” comments Altenseuer.

Until then, several tasks need to be completed, such as improving data connectivity and data quality, before GEA can get started. “So far we have connected 500 machines in the existing portal and we want to transfer Weidmüller's solution to these machines as quickly as possible. I also see great potential for transferring the new technology to other areas at GEA,” she concludes.

Also this week, GEA launched the SmartPacker TwinTube packaging machine. Ideal for packaging small-sized, on-the-go food products, the new high-speed system reduces both operational time, costs and energy across its vertical modules.

Edited by Joshua Poole

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