Mälardalen University

This course explores the integration of artificial intelligence (AI) in decision support systems specifically tailored for the energy and production sectors. Students will learn how AI technologies, such as machine learning, optimization, and data analytics, are transforming traditional operational strategies, enhancing decision-making processes, and driving efficiency in energy and production operations. The curriculum will cover foundational concepts of AI and decision support systems, along with practical applications such as predictive maintenance, demand forecasting, process optimization, and real-time decision support. Through hands-on projects, case studies, and industry-relevant examples, participants will gain insights into designing and implementing AI-driven solutions that improve operational performance, reduce costs, and support sustainability goals. By the end of this course, students will be equipped with the skills to develop and apply AI-driven decision support systems to solve complex challenges in energy and production environments. This course is ideal for professionals and students interested in leveraging AI for operational excellence in the energy and production industries.

This course is designed for engineers, scientists, operators, and managers interested in utilizing AI-based methods for condition monitoring and prognostics in industrial systems and high-value assets. Participants will learn to identify common failure causes and predict Remaining Useful Life (RUL) using historical data, involving tasks such as data processing, feature selection, model development, and uncertainty quantification. Led by experienced professionals from industry and academia, the course covers the basics of prognostics and introduces various AI methods, including deep learning. It represents state-of-the-art AI-driven prognostic techniques, advanced signal processing, and feature engineering methods.

This course provides an understanding of automating software testing using program analysis with the goal of intelligently and algorithmically creating tests. The course covers search-based test generation, combinatorial and random testing while highlighting the challenges associated with the use of automatic test generation. You will learn: Understand algorithmic test generation techniques and their use in developer testing and continuous integration. Understand how to automatically generate test cases with assertions. Have a working knowledge and experience in static and dynamic generation of tests. Have an overview knowledge in search-based testing and the use of machine learning for test generation.

This course teaches you how to build convolutional neural networks (CNN). You will learn how to design intelligent systems using deep learning for classification, annotation, and object recognition. It includes three modules: Image processing: Introduction of industrial imaging through big data and fundamentals of image processing techniques Deep learning with convolutional neural network: Overview of neural network as classifiers, introduction of convolutional neural network and Deep learning architecture. Deep learning tools: Implementation of Deep learning for Image classification and object recognition, e.g. using Keras.

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. You will gain an insight into the possibilities digital twins offer for improving production systems and processes. You will gain an understanding of when the use of digital twins can be a beneficial solution in the development of production systems. This is a course with 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. You can join the course until the end of October.

Today, many industries face an increased demand for designing dependeble systems witch encounters various challenges, including more complex electronics and software-intensive systems. In the course, we will discuss different types of faults and possible sources of faults (technology, human and environment). Different types of faults are handled with different fault tolerance mechanisms, which are discussed for systems, hardware and software components. The course provides a solid foundation for understanding how to design fail-safe systems. The goal is to provide you with a toolbox of concepts for fail-safe design for both hardware and software so you can understand the rationale for appropriate mitigation strategies. The course is suitable for both engineers and students.

This course is designed for you who wants to learn more about functional safety of battery management systems. The course will also cover other aspects of safety such as fire safety in relation to Rechargeable Energy Storage Systems (RESS) and associated battery management systems. In the course you will be able to develop skills in principles of battery management systems, functional Safety as well as of other aspects of safety such as fire safety, hazard identification, hazard analysis and risk assessment in relation to battery management systems. The course also provides a broader understanding of the multifaceted nature of safety. The course is given with a low study pace. This course is primarily intended for engineers that need to ensure that battery management systems are safe, reliable, and compliant with industry standards. The course is suitable for individuals with backgrounds in for example functional safety, battery systems, automotive or risk assessment.  

In this course, you will be made aware of the state-of-the-art in cybersecurity research and state of practice in industry. Cybersecurity vulnerabilities are a threat to progress in the business sector and society. This is an accelerating threat due to the current rapid digitalisation, which in manufacturing is termed Industry 4.0. Companies are aware of this threat and realise the need to invest in countermeasures, but development is hampered by lack of competence.  

If you want to learn principles, methods, tools and project strategies for efficient execution of projects in order to gain high quality competence in industrial project management, this course is for you. The course is suitable for you who work in product development, or with improvements in production, or in an administrative project. You will develop knowledge and skills about how to enhance your project management capabilities and deliver predetermined project results. During the course you will work with and relate what you learn to project management in your company. The phases found in projects are covered; establish, execute, hand-over to line organisation and close-out of the project. Organisational aspects of project management are in focus such as leadership, agility, quality management, communication and decision-making.

Do you want to deepen your knowledge in Industrial Internet of Things? In this course, you will gain deeper knowledge and understanding of the Industrial Internet of Things (IIoT), platforms and cloud services used in manufacturing industries. You will learn to understand the use of IoT platforms and how to design and implement simple systems and how to create value by using IoT solutions within industrial systems. The course will provide you with practical and theoretical knowledge in IIoT, platforms and cloud services as well as in-depth knowledge in production, logistics and product development.

This course provides a fundamental knowledge of IoT, targeting physical devices, communication and computation infrastructure. The course gives theoretical knowledge as well as hands-on experiences to build an IoT application.

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.