Every minute was crucial when a critical machine component failed, requiring the new replacement part to be shipped from SANOVO in Denmark to Brazil. During this time, the sorting plant was at a standstill.

At SANOVO TECHNOLOGY GROUP, one of the world’s leading companies in the development and production of advanced machines and equipment for the egg industry, efforts are being made to avoid similar situations in the future.

Therefore, the company is participating in a project at the national centre for digital technologies (DIREC), where they, together with researchers from the University of Southern Denmark, Aalborg University, and the University of Copenhagen, are investigating how data can be used to detect even small deviations in a production facility.

– If we can somehow get a warning, for example, a month before something happens to a specific component, we can intervene faster and save the customer from the production line coming to a halt,” says Steven Beck Klingberg, System Manager at SANOVO TECHNOLOGY GROUP.

We can probably save a lot of money on travel activities, but otherwise, it will have a significant impact on our customers. If a machine is idle for a week, it can cost the customer several hundred thousand euros in lost production.
– Steven Beck Klingberg, System Manager at SANOVO TECHNOLOGY GROUP

Data reveals irregularities

The company extracts several hundred data points from systems around the world. So far, focus has been on production data, but recently, researchers have shifted their attention to data that reveals the machine’s condition, explains Professor Fabrizio Montesi from SDU, who leads the project.

“We use IoT, edge, and cloud technologies to accumulate data on the function of machines implemented in production and test environments. By analyzing this data, we identify conditions and trends that indicate deviation from normal function. This insight can then be used to predict when a machine needs servicing.”

His colleague on the project, Associate Professor Marco Chiarandini, adds:

– The uniqueness of SANOVO is that the amount of data is large, while errors in the main component are extremely rare. Therefore, classical monitoring and traditional machine learning techniques are not suitable, and we have had to tailor other data science techniques for sequential data analysis.

Aiming to reduce the frequency of maintenance travels

As a side benefit, the project may help reduce the number of maintenance travels, a goal that is important for SANOVO for both environmental and economic reasons.

The company has service personnel employed in Denmark, Holland, Italy, South and North America, Malaysia, Japan, and China – each department has its own area of expertise. A service technician has between 150 and 200 travel days per year, with the entire service organization totaling just over 100 employees.

– If we can predict that a machine will soon need servicing, it will be easier to plan service trips and minimize travel activity – and it will make a difference. We will not only have a better understanding of what is wrong before sending a service technician out into the world, so he can have the right machine parts with him. We also want to catch problems early on, so we can plan smarter and minimize the number of travels, says Steven Beck Klingberg.

Researchers and students dare to challenge

SANOVO’s role in the DIREC project is to contribute expertise on relevant machine data. There are several hundred measurement points in the machines, but not all are significant for the critical components of the machine.

– We have primarily helped researchers figure out which measurement points are important. In that way, we are sparring partners throughout the process, says Steven Beck Klingberg.

There is no doubt that the project is important for the company. Several of SANOVO’s specialists have been involved in the project, which is also followed with great interest by top management.

The collaboration between researchers and a highly specialized company brings a lot of new knowledge and ideas to the table, according to Steven Beck Klingberg.

– Both the researchers and the students we collaborate with are excellent at asking questions that challenge us, and it has been great to get other perspectives along the way. Researchers come with an open mindset and completely new knowledge. It has been fantastic to get some counterplay because you can become a bit narrow-minded when working with the same things in the same industry day in and day out.

The researchers also see great value in the collaboration.

– Identifying a project of concrete value to Sanovo has been the key to gaining support and interest from the right people in the company, which has been crucial for the success of the collaboration. All parties have been quite open in the research phase, and we all benefit from the new experience and knowledge exchange, says Fabrizio Montesi.