This course is an online winter school course that provides a comprehensive understanding and hands-on practices to apply industry 4.0 transformation.
Industry 4.0 transformation is the process to leverage digitisation and other technologies to enable the system to perceive (sensing) its behaviour and cognitize, automate, autonomize, and assist its operations. This transformation process requires several enabling technologies: IoT, Edge/Cloud computing, data cognition analytics (machine learning, big data, deep learning, artificial intelligence), Predictive and Simulation Analytics (Predictive maintenance, Digital twinning), remote operated vehicles (drone, vessel, robot, mobot, cobot), additive manufacturing and 3D printed spare parts, and Virtual and Augmented reality.
Industry 4.0 transformation has principles, reference architectures (e.g. RAMI 4.0), maturity assessment models, project execution models, enabling technologies and engineering and management methods.
It is a project-based learning course.
- The course provides industry 4.0 fundamentals: Principles, drivers, dimensions, architectures, technologies, roadmaps and applications.
- The course provides the project execution model to transform business processes, assets and products to comply with the industry 4.0 vision. This covers exploration, feasibility study, concept and Concept and front-end engineering (FEED) studies.
- The course provides hands-on technical and management methods, e.g. Systems analysis, Use-case analysis, Readiness/Maturity analysis, Big-picture evaluation matrix, Industry 4.0 concept study, Operational architecture modelling.
- The course provides demonstrations on several enabling technologies: IoT technology using Arduino/Azure kits, Machine learning using Azure ML, predictive maintenance using Open Modelica/Simulink/Anylogic, Augmented reality using Unity, Open-source information management system using Odoo.
By completing this course, the students shall
- Gain a good understanding of Industry 4.0 fundamentals: Principles, drivers, dimensions, and architectures.
- Gain a basic understanding and theories behind the Industry 4.0 Transformation process and existing roadmaps.
- Gain a basic understanding and theories behind the enabling technologies: IoT, Edge/Cloud computing, data analytics, Predictive maintenance, Digital twinning, remote operated vehicles, 3D printed spare parts, Virtual/Augmented reality and Open-source information management systems.
- Be able to apply the project execution model to design or transform Industry 4.0-complied business processes and equipment.
- Be able to synthesize, conceptualise and deliver Concept and front-end engineering (FEED) studies of newly transformed business processes or equipment.
- Be able to perform engineering analysis methods, e.g. systems analysis, use-case analysis, readiness/maturity analysis, big-picture evaluation matrix, industry 4.0 concept study, operational architecture modelling.
- Be able to demonstrate new Industry 4.0 concepts using Arduino/Azure kits, Azure ML, open Modelica/Simulink/Anylogic, Unity, Odoo.
- 26.11. 2021, Kick-off meeting, Introduction lecture for the winter school, 10;00-12;00. Then the student will have two weeks to study module 1 (on Canvas) and submit first assignment.
- 13.01.2022, Lecture day (full day).
- 14.01.2022, Technology demonstration day (full day) and kick-off the course group projects.
- 04.02.2022, Course Project Submission.
- 11.02.2022, Project presentation day
- 25.02.2022, Grades submission
Expectation and registration
Students are expected to:
- Study the theory-module and perform an online assignment.
- Attend remotely the lecture and demonstration days and work in groups.
- Submit course project assignment.
- Present course project assignment.
- For UiS and ASOIU students, the OFF705 course will be credited 5 Ects.
- For NU students, it will be credit 6 Ects.
- Please register via this link by 01.11.2021
- The course committee will notify accepted students by 15.11.2021
- Aytan Mursaguliyeva
- Asad Safarov
- Anastassiya Reshodko
- Maida Khuzhaniyazova
- Medet Kospagambetov
- Nurgabyl Khoyashov
- Aizhan Serikova
- Kebir Mohammed Jemal
- Majid Fouad Ahmed Shalfa
- Assel Nartay
- Nurzhan Maldenov
- Bibizhan Orazaliyeva
- Tosin Bankole-Oye
- Indira Kabimoldina
- Aidos Shaimenov
- Assel Jumassultan
- Narmin Mirzayeva
- Amin Elmenshawy
- Askar Zhanaliyev
- Temidayo Boboye
- Alisher Aliyev
- Saeed Behjat
- Jan Benedict Bullecer
- Vanessa Sele
- Islam Mukhammedrakhym
- Ellada Bayramova
- Assiya Issabekova
- Aizat Ongarbayeva
- Aliya Bazarbekova
- Vusal Iskandarov
- Nazerkem Abilman
- Durbek Abduvali
- İlkinaz Alishli
- Mahmood Al-Dayekh
- Kymbat Khamit
- Akmalluddin Shah Muhammad
- Edgar Enrique Casanova
- Mohammad Ghasemi
- Mahnaz Noorizadeh
- Shynggys Bimagambetov
- Firuza Miriyeva
- Turan Abdullayev
Note: Master students have been prioritized for this winter school.