Course content

This course provides an in-depth exploration of cyber-physical systems (CPS) within production environments, focusing on the integration of digital and physical systems to create efficient, automated, and intelligent production processes. Emphasizing a hands-on approach, the course is centered on learning by doing, with extensive practical work conducted in a learning factory lab. Students will engage with real-world applications through guest lectures from industry experts and visits to industry sites, allowing them to see CPS concepts in action. Topics include foundational CPS principles, practical machine-to-machine (M2M) communication protocols, energy sensor integration, and artificial intelligence applications. Additionally, students will gain hands-on experience with real-time data platforms, basic PLC programming, and cybersecurity practices essential for maintaining secure CPS operations.

Learning outcome

Knowledge

• Develop a foundational understanding of cyber-physical systems, including core components and interactions within production settings.
• Understand and explain key M2M communication protocols, with a focus on OPC-UA.
• Gain knowledge of energy sensors, their applications, and the role they play in energy management within CPS.
• Comprehend the importance of OT-IT integration for data-driven decision-making and process optimization.
• Identify and describe AI applications that enhance CPS performance.
• Recognize cybersecurity threats specific to CPS and understand best practices for securing these systems.

Skills

• Configure and implement basic machine-to-machine communication protocols, specifically using OPC-UA.
• Develop and test basic PLC programs to control physical systems within a production environment.
• Integrate and use energy sensors within production systems for real-time monitoring and data analysis.
• Utilize OT-IT data platforms to collect, analyze, and interpret data from various sources in a CPS.
• Apply AI algorithms to analyze data and improve production processes.
• Identify potential cybersecurity risks in CPS and implement protective measures.

General Competence

• Develop problem-solving skills for tackling complex issues in CPS, including both technical and security challenges.
• Enhance collaborative skills by working on multidisciplinary teams focused on integrating CPS within production.
• Build a sustainable mindset by understanding energy-efficient practices in CPS.
• Demonstrate ethical awareness in handling data and ensuring secure, responsible implementation of CPS technologies.
• Stay informed about advancements in CPS, AI, and cybersecurity for continuous professional development.

Learning methods and activities

Teaching Methods:

• Interactive seminars utilizing the flipped classroom approach
• E-learning modules for flexible learning
• Project-based work to apply theoretical knowledge
• Collaborative group work to enhance teamwork skills
• Guest lectures from industry experts to provide real-world insights

The course is designed to be accessible to both on-campus and remote students. Each student can choose the pedagogical arrangement that best fits their needs. Seminars will be conducted on campus and are also available via streaming through Blackboard Collaborate/Teams, with recordings accessible through NTNU's learning management system. Tutoring will be available both on-campus and online at scheduled times. The medium of instruction is Norwegian/English, and all assignments, reports, and documentation must be submitted in Norwegian/English.

Specific conditions

Required previous knowledge

Basic knowlege of manufacturing systems and machine learning

Course materials

Relevant articles and reports will be given at course start