Our 20,000 students read courses and study programmes in Business, Health, Engineering and Education. We conduct research within all areas of education and have internationally outstanding research in future energy and embedded systems. Our close cooperation with the private and public sectors enables us at MDU to help people feel better and the earth to last longer. Mälardalen University is located on both sides of Lake Mälaren with campuses in Eskilstuna and Västerås.
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In this course, you will learn how data analysis in virtual production can improve your organization's results! Data analytics in virtual production uses advanced techniques to collect, analyze and present data to improve production. This system is designed to help companies optimize their production and increase efficiency. By learning how to model, do scenario analysis and evaluate using industrial software, identify bottlenecks, and use AI methods and applications, s necessary to succeed with a full production analysis. The course is given with flexible start and study pace, but we recommend a study pace of 20 %, which means that the course takes about 8 calendar weeks.
Learn about digital twins and how they can be used in smart production! A digital twin is used to create a virtual model of a real production system. Among other things, it can be used to simulate how the product will be manufactured, how materials flow and how machines move. The course gives you knowledge of industrial digital twins and their application within the framework of smart production. The course is given with flexible start and study pace, but we recommend a study pace of 20%, which means that the course takes about 8 calendar weeks.
The rapid development of digital technologies and advances in communications have led to gigantic amounts of data with complex structures called ‘Big data’ being produced every day at exponential growth. The aim of this course is to give the student insights in fundamental concepts of machine learning with big data as well as recent research trends in the domain. The student will learn about problems and industrial challenges through domain-based case studies. Furthermore, the student will learn to use tools to develop systems using machine-learning algorithms in big data.
Learn how to improve industrial processes with modelling methods! Modeling is used to create a virtual representation of a real product. With the help of the model, you can study how the product works, test different options and evaluate the product before it is produced in reality. In this course, you gain knowledge on how to design and implement simulation models in the work of analyzing and improving production systems. You will learn how to plan and perform improvement studies, as well as apply the modeling process within the manufacturing industry. The course is given with flexible start and study pace, but we recommend a study pace of 20%, which means that the course takes about 8 calendar weeks.
The aim of the course is to introduce the participants into methods and tools for verifying systems that need to react to external stimuli. The methods use system models with precise formal semantics and will span model-checking as well as deductive verification. A set of simple examples as well as real-world applications will be used throughout the course to illustrate the methods and their tool support. The objective of the course is to understand the underpinning theories of formal verification, and learn how to apply tool support in order to verify system models.
The purpose is to give the students an overview of issues and methods for development and assurance of safety-critical software, including details of selected technologies, methods and tools. The course includes four modules: Introduction to functional safety; knowledge that give increased understanding of the relationship between Embedded systems / safety-critical system / accidents / complexity / development models (development lifecycle models) / certification / “the safety case”. Analysis and modelling methods; review of analysis and modelling techniques for the development of safety-critical systems. Verification and validation of safety critical software, methods and activities to perform verification and validation. Architectures for safety critical systems. Safety as a design constraint.
This course makes you acquainted with the concept of systems-of-systems (SoS), which means that independent systems are collaborating. It gives you an understanding why SoS is an important topic in the current digitalisation and provides a theoretical and practical foundation for understanding important characteristics of SoS. It also gives you a deeper knowledge in a number of key concerns that need to be considered when engineering SoS. Admitted students may join the course any time between September 2 and October 6, 2024. With the recommended study pace of 25%, the course would take approximate seven calendar weeks to complete. Higher or lower study pace is possible as long as the course is finished no later than January 19, 2025.