Impulses for the digital change
Research council visited SCCH
Between 2008 and 2013 the value added in the area of "Information & Communication" rose by an average of 7.3% in Upper Austria, almost three times as strong as the Austrian average of 2.5%. The number of employees in this economic segment also rose by 7.8% in the period from 2011 to 2013 (Statistik Austria 2016), thus showing the highest growth in the industry.
"As a research, training and economic location, the Softwarepark Hagenberg contributes significantly to the high innovative power of Upper Austria in IT," emphasizes LH-Stv. Dr. Strugl. He was able to convince himself of this during his visit through the Upper Austrian research landscape, coordinated by Upper Austrian Research GmbH (UAR), the research manager of the state of Upper Austria.
v.l.t.r.: Dr. O.Univ.Prof. Dipl.-Ing. Dr.techn. A Min Tjoa, LH-Stv. Dr. Michael Strugl, MBA, DI Dr. Klaus Pirklbauer, DI Dr. Wilfried Enzenhofer, MBA (Copyright: Land OÖ/ Heinz Kraml)
Predictive maintenance at SCCH
The Software Competence Center Hagenberg (SCCH) conducts research and innovation in software engineering and data science. This combination reflects exactly the requirements of industry 4.0 and digitalization. The main fields of application are industrial production as well as the production of machinery and equipment. Since the founding of the SCCH in 1999, the COMET K1 Competence Center has been focusing on application-oriented research and development. The close cooperation with numerous well-known companies from industry and industry makes the SCCH a prime example for a well-functioning orientation along the 'innovation chain' of education, research and business.
Among other things, the SCCH deals intensively with the subject of "Smart Maintenance". The predictive maintenance of production equipment is a key value driver in the industry and an elementary building block in the industrial production environment. In general, it is necessary to reduce the costs for maintenance processes, to completely avoid production losses and to significantly increase efficiency. Methods from data mining and machine learning are used. Project partners are well-known industrial companies such as BMW Motoren and BRP-Rotax as well as Messfeld and the Montanuniversität of Leoben.