Intelligent Industrial Systems
Objectives
This curricular unit is focused on concepts and technologies of artificial intelligence in industrial systems, specifically regarding their application in predictive and smart manufacturing. At the of this unit students will have acquired knowledge, skills and competences that will allow them to:
-Understand:
1) Core concepts and methods of intelligent industrial systems;
2) The impact of technology and their activity in a social, environmental and economical context;
-Be capable of:
1) Planning, designing and implementing predictive and decision-support solutions for industrial settings;
2) Bridging the gap between the latest technological advances in intelligent systems and the industry;
3) Integrating inter- and multidisciplinary knowledge with rigor and security;
-Know:
1) The industrial reality, enabling an active role bridging industry and research in the field;
2) The main challenges and barriers in the adoption of intelligent industrial systems.
General characterization
Code
13091
Credits
3.0
Responsible teacher
José António Barata de Oliveira, Luís Manuel Camarinha de Matos
Hours
Weekly - Available soon
Total - 56
Teaching language
Inglês
Prerequisites
Knowledge on programming and intelligent supervision.
Bibliography
- Course handouts (slides and tutorials);
- Suh, S. C., Tanik, U. J., Carbone, J. N., & Eroglu, A. (Eds.). (2014). Applied cyber-physical systems. Springer New York.
- Lee, J. (2020). Industrial AI. Springer, Singapore.
- Selection of relevant scientific articles.
Some examples:
- Lee, J., Jin, C., & Bagheri, B. (2017). Cyber physical systems for predictive production systems. Production Engineering, 11(2), 155-165.
- Peres, R. S., Jia, X., Lee, J., Sun, K., Colombo, A. W., & Barata, J. (2020). Industrial Artificial Intelligence in Industry 4.0-Systematic Review, Challenges and Outlook. IEEE Access, 8, 220121-220139.
- Peres, R. S., Rocha, A. D., Leitao, P., & Barata, J. (2018). IDARTS–Towards intelligent data analysis and real-time supervision for industry 4.0. Computers in industry, 101, 138-146.
Teaching method
This curricular unit includes a theoretical component and a laboratory component of equal duration. The theoretical component contemplates dynamic classes combining a small expositive part with activities geared towards discussion and reflection, creating the conditions necessary for students to take on an active and central role in their learning process. The laboratorial component includes several integrative group projects combining knowledge from the different models of the curricular unit in the resolution of applied problems.
Evaluation method
The evaluation of the theoretical component (TC) is continuous, carried out through 4 mini-tests throughout the semester. The laboratorial component (LC) results from the development of the assignments throughout the semester, culminating in their oral presentation. There is also a summative component (SC) resulting from quizzes delivered at the end of each theoretical session.
The final grade can be calculated by FG = 0.4*TC + 0.55*LC + 0.05*SC
Subject matter
1) Introduction to Intelligent Industrial Systems
2) Applied Machine Learning
3) Reference Models
4) Intelligence in Cyber-Physical Systems
4.1) Self-Learning
4.2) Self-Adaptation
5) Data Governance
5.1) Interpretability
5.2) Privacy-Preserving Mechanisms
5.3) Fairness
6) Data Visualization and Human-Machine Interfaces
7) Real Applications of Intelligent Industrial Systems
7.1) Predictive Maintenance
7.2) Energy Consumption Optimization
7.3) Intelligent Quality Control
8) Synthetic Data
8.1) Sampling Techniques
8.2) Generative Adversarial Networks (GAN)
9) Seminar with Industry Expert
Programs
Programs where the course is taught: