Mälardalen University

The aim of the course is to provide proficiency in cybersecurity analysis and design in industrial settings, with a special focus on smart factories and Industry 4.0. To achieve this, you will learn about advanced cybersecurity concepts, methodologies and tools. You will also be able to apply your knowledge to industrial case studies.

The aim of this course is to provide participants with the principles behind model-driven development of software systems and the application of such a methodology in practice. Modelling is an effective solution to reduce problem complexity and, as a consequence, to enhance time-to-market and properties of the final product.

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. This is a course with a flexible start: 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 course aims to give insights in fundamental concepts of machine learning for predictive analytics to provide actionable, i.e., better and more informed decisions in, forecasting. It covers the key concepts to extract useful information and knowledge from data sets to construct predictive modeling. The course includes three modules: Introduction: overview of Predictive data analytics and Machine learning for predictive analytics. Data exploration and visualization: presents case studies from industrial application domains and discusses key technical issues related to how we can gain insights enabling to see trends and patterns in industrial data. Predictive modeling: consists of issues in construction of predictive modeling, i.e., model data and determine Machine learning algorithms for predicative analytics and techniques for model evaluation.

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 aim of this course is to give students insight about certification and about what it means to certify/self-assess safety- critical systems with focus on software system and to create a safety case, including a multi-concern perspective when needed and reuse opportunities, when appropriate.

This course deals with model-based testing, a class of technologies shown to be effective and efficient in assessing the quality and correctness of large software systems. Throughout the course the participants will learn how to design and use model-based testing tools, how to create realistic models and how to use these models to automate the testing process in their organisation.

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.  

In this course you will learn state-of-the-art statistical modelling for the purpose of analysing industrial data. The course also presents the basics of relational databases and data manipulation techniques needed to prepare the data for analysis.

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. This is a course with a flexible start: If you are admitted, you may join the course any time between the course start in September 2025 until the beginning of October. With the recommended study pace of 25%, the course will take approximately seven calendar weeks to complete. Higher or lower study pace is possible as long as the course is finished no later than the end of the autumn semester.  

AI systems are increasingly being integrated into various industrial processes, including manufacturing, logistics, and autonomous vehicles. Trustworthy AI ensures that these systems operate reliably, reducing the risk of accidents or costly errors.  Trustworthy AI helps companies comply with ethical standards and legal regulations. It ensures that AI systems do not discriminate against certain groups, violate privacy rights, or engage in other unethical behaviors. Trustworthy AI System course can support in the development of more advanced AI technologies, fostering research collaboration, and attracting talent. 

Virtual commissioning (VC) is a technique used in the field of automation and control engineering to simulate and test a system's control software and hardware in a virtual environment before it is physically implemented. The aim is to identify and correct any issues or errors in the system before deployment, reducing the risk of downtime, safety hazards, and costly rework. The virtual commissioning process typically involves creating a digital twin of the system being developed, which is a virtual representation of the system that mirrors its physical behaviour. The digital twin includes all the necessary models of the system's components, such as sensors, actuators, controllers, and interfaces, as well as the control software that will be running on the real system. Once the digital twin is created, it can be tested and optimized in a virtual environment to ensure that it behaves correctly under various conditions. The benefits of using VC include reduced project costs, shortened development time, improved system quality and reliability, and increased safety for both operators and equipment. By detecting and resolving potential issues in the virtual environment, engineers can avoid costly and time-consuming physical testing and debugging, which can significantly reduce project costs and time to market. The course includes different modules, each with its own specific role in the process. Together, the modules create a comprehensive virtual commissioning process that makes it possible to test and validate control systems and production processes in a simulated environment before implementing them in the real world. Modeling and simulation: This module involves creating a virtual model of the system using simulation software. The model includes all the equipment, control systems, and processes involved in the production process. Control system integration: This module involves integrating the digital twin with the control system, allowing engineers to test and validate the system's performance. Virtual sensors and actuators: This module involves creating virtual sensors and actuators that mimic the behavior of the physical equipment. This allows engineers to test the control system's response to different scenarios and optimize its performance. Scenario testing: This module involves simulating different scenarios, such as equipment failures, power outages, or changes in production requirements, to test the system's response. Data analysis and optimization: This module involves analyzing data from the virtual commissioning process to identify any issues or inefficiencies in the system. Engineers can then optimize the system's performance and ensure that it is safe and reliable. Expected outcomes Describe the use of digital twins for virtual commissioning process. Develop a simulation model of a production system using a systems perspective and make a plan for data collection and analysis. Plan different scenarios for the improvement of a production process. Analyze data from the virtual commissioning process to identify any issues or inefficiencies in the system and then optimize the system's performance. Needs in the industry Example battery production: Battery behaviors are changing over time. To innovate at speed and scale, testing and improving real-world battery phenomena throughout its lifecycle is necessary. Virtual commissioning / modeling-based approaches like digital twin can provide us with accurate real-life battery behaviors and properties, improving energy density, charging speed, lifetime performance and battery safety. Faster innovation (NPI) Lower physical prototypes Shorter manufacturing cycle time Rapid testing of new battery chemistry and materials to reduce physical experiments Thermal performance and safety It’s not just about modelling and simulating the product, but also validating processes from start to finish in a single environment for digital continuity. Suggested target groups Industry personnel Early career engineers involved in commissioning and simulation projects Design engineers (to simulate their designs at an early stage in a virtual environment to reduce errors) New product introduction engineers Data engineers Production engineers Process engineers (mediators between design and commissioning) Simulation engineers Controls engineer System Integration