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SPM Instrument in project collaboration on industrial maintenance with AI and machine learning

SPM Instrument has entered into a project collaboration with Mälardalen University, among others, to develop a new type of system for AI-based prediction of the operating condition of industrial machinery, which can contribute to more efficient maintenance work in Swedish industries. The CPMXai project is conducted within the framework of Produktion2030, coordinated by Teknikföretagen with grant financing through Swedish innovation agency Vinnova.

Innovation and research for sustainable growth in Swedish industry

Sweden's innovation agency Vinnova, which is also the government's expert authority in the area of ​​innovation policies, is tasked with strengthening the country's innovation capacity to contribute to sustainable growth. The agency stimulates cooperation and innovative power and gives various actors the opportunity to experiment and test new ideas – all with the aim of facilitating the transition to a sustainable future.

Vinnova invests approximately SEK 3 billion annually in research and innovation projects. This is mainly done through announcements where companies and organizations can apply for funding – among other things through the strategic innovation program Produktion2030, which is supported by Vinnova, the Swedish Energy Agency, and Formas. Within the framework of this program, SEK 41 million was announced in 2021.

Sustainability as a driving force for meeting challenges

Against the background of the global sustainability goals in Agenda 2030, the goal of the innovation program Produktion2030 is to create a national base of research, innovation, and education to ensure the competitiveness of Swedish industry 2030.

Produktion2030 is based on six long-term challenges for the Swedish manufacturing industry. With the goal that Swedish industries must be sustainable and competitive, industry, academia, and research institutes work to meet these challenges with the support of the program.

Virtual production development - a challenge for Swedish industry

One of the challenges defined by Produktion2030 is “Virtual production development”, which is about transforming information and data into knowledge and decision support in virtual and physical production systems. Within this challenge area, the research and innovation project CPMXai*) has been awarded approximately SEK 6 million to develop a digital twin – a copy in a computer environment – of a selected machine in a production environment for three years. Measurement data from the real machine regarding current operating condition will be implemented on the digital twin in order to develop a new, automatic tool for marking condition data, which in turn will form the basis for a self-monitoring, self-learning, and self-explanatory system for the prediction of machine condition.

SPM contributes sensors, measuring systems, and software for collecting condition data from the machine that will form the basis for the digital twin. Furthermore, SPM is responsible for configuring suitable measuring assignments for the machine in question as well as knowledge regarding the integration of measurement data to other systems.

Johan Nilsson, CTO at SPM Instrument, about the project: “Every year, SPM invests large resources in its own research and development in industrial condition monitoring. Our participation in this exciting project is an opportunity for us to strengthen and develop our collaboration with the university and our other project partners, where we can also learn from each other. The project is in line with our ambition to continue the development of machine learning and AI in our solutions. We see great potential in streamlining industrial maintenance and highlighting condition monitoring as an effective tool for increased sustainability in the industry with the help of easily accessible and easy-to-understand AI.

The project consortium for CPMXai consists of Mälardalen University (MDU) as coordinator, Hitachi High-Tech Europe GmbH, Adopticum, Nordic Electronic Partner (NEP), GKN Driveline Köping AB, RISE Research Institutes of Sweden AB, and SPM Instrument.

*) Cognitive Predictive Maintenance and Quality Assurance using EXplainable AI and Machine Learning