Machine errors can be predicted when companies and researchers collaborate

Industrial PhD student Pablo del Moral at Halmstad University is active at Getinge Sterilization AB and has conducted the research project PRIME (Predictive Intelligent Maintenance Enabler) for the past five years. The goal has been to predict when machines need care and repair, so-called predictive maintenance, in other words before the machine breaks down.

“Tests and analyses have been carried out on the sterilizers that the Getinge company manufactures. Sterilizers are important in hospitals, and being able to predict errors before they occur is very valuable”, says Pablo del Moral.

Predictive maintenance can save time and money

Today, it is common to collect data using sensors on, for example, vehicles, sterilizers and various types of industrial equipment. The PRIME project develops ways to create models so that the data can be used to predict damage, wear and tear and errors. An important question to answer concerns exactly what the collected data can help to predict. It is also important to identify the types of errors that cannot be predicted, so that methods for different scenarios can be developed.

“We try to predict errors by studying the machines' operating data. At the moment, such data cannot monitor the status of the machines in the way we want in PRIME, but are designed based on other reasons such as safety, compliance with regulation and control of processes”, says Pablo del Moral.

With predictive maintenance, reliability can increase and operating costs decrease. This is done by getting information in advance about what is about to break in a machine and when. It improves the operating time of machines, which increases productivity.

“The goal is to be able to predict faults in a machine before they actually occur. This enables us to plan non-disruptive maintenance measures in the machines, which reduces downtime and increases operation. By not routinely replacing components that still work well, costs are also reduced. In the future, it can also decrease the number of incorrect maintenance measures and facilitate the diagnosis and repair process performed by technicians in the field”, says Pablo del Moral.

The knowledge developed in the PRIME project on predictive maintenance can be used to determine where new sensors should be placed to improve the overall performance of the preventive maintenance system.

The project involves many parties

PRIME is an example of a real collaboration project, not only between Getinge Sterilization AB and the University. Several people from different departments within Getinge Sterilization AB have also been involved in the project since the start, from service teams in France and Sweden to AI teams in Denmark and Ukraine. Before the pandemic broke out, there was also a collaboration with New Karolinska Solna, who shared their results.

“Getinge is a good example of a traditional manufacturing company that is going digital. The company has a huge amount of information about the machines and their processes. Without our close cooperation, the PRIME project would not have been possible”, says Pablo del Moral.

The President at Life Science Getinge AB, Harald Castler, agrees with the importance of PRIME:

“If we can solve the problems studied within PRIME, it would be a big step forward for both Getinge and our customers”, says Harald Castler.

Text: Anna-Frida Agardson
Top illustration: Getinge AB (Anders Sällström)
Portrait photo: Roland Thörner