Academic year 2022 - 2023

•   Development of modern automation systems AT22IS04-3001 09.01.2023-19.04.2023  5 credits  (AT22HP-V) +-
  Competence objectives of the study unit

  The development of modern systems for process automation and controls involves understanding a chain of events. Proper understanding of research plans, specifications, project work and management are fundamental elements for the development of control systems. The student will learn how to setup requirements and structures for development projects. The student will also understand the importance of life cycle planning and the effects of obsolescence. Supervision, data acquisition, controls, communication and networks are subjects that will be studied in the course. The student will learn the process and steps needed for a successful development of a modern automation system.
  
  Knowledge and understanding
  After completing the course, the student should be able to:
  - identify and discuss requirements needed for automation system development projects
  - describe how life cycle and obsolescence management can be applied to automation systems
  - recognize the different main areas for a modern automation system.
  
  Skills and abilities
  After completing the course, the student should be able to:
  - conduct requirements collection for automation system development projects
  - apply obsolescence management principles and common practices
  - analyze the needs and required elements for developing a modern automation system.
  
  Evaluation ability and approach
  After completing the course, the student should be able to:
  - plan the development for a modern automation system
  - assess and report on a system’s life cycle state in terms of obsolescence
  - propose solutions for lifetime extension and modernization of automation systems.

  Prerequisites

  No prerequisites

  Content of the study unit

  The course content focuses on needs and requirements for developing a modern automation system that is capable of hosting modern elements. Aspects related to sustainability and life cycle management for automation systems are studied. Concepts and methods for managing requirements and obsolescence are central parts.

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Satisfactory skills in the development of automation systems

  Assessment criteria – good (3-4)

  Good skills in the development of automation systems

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Teaching language

  English

  Timing

  09.01.2023 - 19.04.2023

  Enrollment date range

  01.12.2022 - 15.01.2023

  • 5 credits

  Group(s)

  • AT22HP-V

  Teachers and responsibilities

  Jan Berglund, Joachim Böling

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Automation Technology

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

•   Dynamic systems – modeling, simulation and control AT22IS02-3001 29.08.2022-27.12.2022  5 credits  (AT22HP-V) +-
  Competence objectives of the study unit

  Systems thinking is the holistic approach to a field of science, where cause and effect are entangled in a feedback network. The field of automation relies on systems thinking in the way it examines the feedback system and process dynamics in general. Ways of describing these dynamic systems are through modeling and identification. The course aims at introducing the above to the students to achieve a common platform of knowledge.
  Knowledge and understanding
  After completing the course, the student should be able to:
  - recognize and describe common dynamic process systems
  - explain the difference between various systems
  - describe the practical usage of some process models
  
  Skills and abilities
  After completing the course, the student should be able to:
  - create and simulate process models
  - experiment with different process models in automation and control
  - perform basic identification of processes
  
  Evaluation ability and approach
  After completing the course, the student should be able to:
  - justify the choice of a process model
  - evaluate and report on the choice of a model

  Prerequisites

  No prerequisites

  Content of the study unit

  The contents of the course are concept and principles of dynamic systems, system modeling and simulation, techniques for system identification, optimization methods and feedback control.

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Satisfactory skills in systems thinking, dynamic systems, modeling, identification, optimization and control.

  Assessment criteria – good (3-4)

  Good skills in systems thinking, dynamic systems, modeling, identification, optimization and control.

  Assessment criteria – excellent (5)

  Excellent skills in systems thinking, dynamic systems, modeling, identification, optimization and control.

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Learning material

  See moodle page or course info

  Learning methods

  Lectures, exercises and laboratory work

  Objects, timing and methods of assessment

  See moodle page or course info on peppi

  Teaching language

  English

  Timing

  29.08.2022 - 27.12.2022

  Enrollment date range

  15.06.2022 - 11.09.2022

  • 5 credits

  Group(s)

  • AT22HP-V

  Teachers and responsibilities

  Joachim Böling, Ray Pörn

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Automation Technology

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

  Alternative methods of attainment for implementation

  Assignments, exams or group work

  Student's schedule and workload

  A total of 125 h

•   Intelligent systems AT22IS05-3001 13.02.2023-14.05.2023  5 credits  (AT22HP-V) +-
  Competence objectives of the study unit

  The course focuses on modern intelligent systems in process automation and control. The goal is to give insights and understanding of the design and usage of intelligent systems in automation.
  
  Knowledge and understanding
  After completing the course, the student should be able to:
  - explain the basic steps and functionality of intelligent systems for automation
  - describe the challenges and opportunities with intelligent systems
  
  Skills and abilities
  After completing the course, the student should be able to:
  - analyze the needs and requirements for an intelligent system
  - practice with different methods for realizing intelligent systems
  
  Evaluation ability and approach
  After completing the course, the student should be able to:
  - plan the development of an intelligent system
  - propose and evaluate solutions for intelligent systems

  Prerequisites

  No prerequisites

  Content of the study unit

  The course focuses on different ways of designing and implementing intelligent systems in a process and automation context. Important steps of an intelligent system are data collection, preparation, visualization (monitoring) and the final step of automated decision making. Examples of automated decision making are detection of missing data, identification of faults and malfunctions, classification tasks, process optimization and system control. The content could explicitly include data-driven and model-driven fault detection, principles of interface design, digitalization and cyber security issues and aspects concerning IoT solutions.

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Satisfactory skills in understanding and development of intelligent systems

  Assessment criteria – good (3-4)

  Good skills in understanding and development of intelligent systems.

  Assessment criteria – excellent (5)

  Excellent skills in understanding and development of intelligent systems.

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Teaching language

  English

  Timing

  13.02.2023 - 14.05.2023

  Enrollment date range

  01.12.2022 - 16.02.2023

  • 5 credits

  Group(s)

  • AT22HP-V

  Teachers and responsibilities

  Ray Pörn, Kaj Wikman

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Automation Technology

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

•   Machine learning methods in automation AT22IS03-3001 24.10.2022-30.12.2022  5 credits  (AT22HP-V) +-
  Competence objectives of the study unit

  Machine learning methods have become an important tool for automation, control and data-driven decision making in an industrial context. The course aims at introducing and practicing several of these tools for system identification, classification and predictive analytics.
  
  Knowledge and understanding
  After completing the course, the student should be able to:
  - identify and recognize industrial problems that are solvable through machine learning
  - plan and apply the machine learning workflow to classification, regression and identification problems
  - understand and explain the difference between different machine learning tasks and techniques
  
  Skills and abilities
  After completing the course, the student should be able to:
  - develop and build machine learning models for different tasks
  - validate and test the performance of a model
  - develop hands-on experience with machine learning techniques
  
  Evaluation ability and approach
  After completing the course, the student should be able to:
  - interpret the result of a machine learning experiment
  - report on the result of a machine learning experiment
  - assess the outcome of a machine learning experiment

  Prerequisites

  No prerequisites

  Content of the study unit

  The availability of big amounts of data for various systems makes it possible to apply both linear and nonlinear models to fit the data. The course focuses on different techniques that can be used for identification, classification and predictive purposes in the area of automation and control.

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Satisfactory skills in understanding and using machine learning tools for classification, prediction and identification.

  Assessment criteria – good (3-4)

  Good skills in understanding and using machine learning tools for classification, prediction and identification.

  Assessment criteria – excellent (5)

  Excellent skills in understanding and using machine learning tools for classification, prediction and identification.

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Learning material

  See moodle page

  Learning methods

  Lectures, exercises, laboratory work

  Objects, timing and methods of assessment

  See moodle page or course info on peppi

  Teaching language

  English

  Timing

  24.10.2022 - 30.12.2022

  Enrollment date range

  16.06.2022 - 26.10.2022

  • 5 credits

  Group(s)

  • AT22HP-V

  Teachers and responsibilities

  Ray Pörn

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Automation Technology

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

  Student's schedule and workload

  A total of 125 h

•   Master's Thesis AT22MT07-3001 22.08.2022-31.12.2023  30 credits  (AT21HP-V) +-
  Competence objectives of the study unit

  The degree thesis is part of the student's personal competence profile. In the degree thesis, the student will apply theoretical models to practical problems. The student must also show that they can handle complex problems independently within their area of competence and can work logically and methodically. The student should be able to work in a scientific way and use reference material and external sources of knowledge.

  Prerequisites

  The course Research methods and scientific writing or equivalent knowledge and at least 15 credits of compulsory studies performed.

  Content of the study unit

  The thesis consists of a practical application and an associated theoretical analysis. The written design must follow Novia's writing instructions for a degree thesis. The thesis also includes a public presentation of the thesis and a written maturity test.
  
  The degree thesis can be started when students have sufficient knowledge in the form of approved professional studies and methodology studies. The work begins with students drawing up a work and / or research plan for the process.The plan presents at least subject choices, language, work title, a preliminary outline and schedule. The plan must be approved by the head of the degree program. In connection with the approval of the plan, supervisors are appointed for degree work.
  
  The degree thesis may consist of internal and / or external ordering work, collaborative projects or other work-related work. These may be, for example:
  - an investigation (e.g. investigation, survey, analysis and / or evaluation)
  - development work (e.g. development of working methods or activities)
  - product development (e.g. prototype or model)

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  The student's degree thesis satisfies the assessment criteria applied in this degree program at a satisfactory level. The assessment criteria can be found on the Moodle page for thesis work concerned year.

  Assessment criteria – good (3-4)

  The student's degree thesis satisfies the assessment criteria applied in this degree program at a good level. The assessment criteria can be found on the Moodle page for thesis work concerned year.

  Assessment criteria – excellent (5)

  The student's degree thesis satisfies the assessment criteria applied in this degree program at an excellent level. The assessment criteria can be found on the Moodle page for thesis work concerned year.

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Teaching language

  English

  Timing

  22.08.2022 - 31.12.2023

  Enrollment date range

  15.06.2022 - 20.12.2022

  • 30 credits

  Group(s)

  • AT21HP-V

  Teachers and responsibilities

  Hanna Kuusisto, Jan Berglund, Joachim Böling, Ray Pörn, Kaj Wikman

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Automation Technology

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

•   Project course in automation AT22IS06-3001 10.04.2023-04.06.2023  5 credits  (AT22HP-V) +-
  Competence objectives of the study unit

  To participate in and complete a project in some area of the industrial automation process. The student group will learn to identify and describe the problem/task, propose, plan and implement a solution approach, evaluate and assess the result and report on the findings.
  
  Knowledge and understanding
  After completing the course, the student should be able to:
  - identify and explain the problem/task and a possible solution approach
  - discuss and compare different solution approaches
  
  Skills and abilities
  After completing the course, the student should be able to:
  - propose and plan possible solution approaches
  - create an intelligent system for automation
  - apply and experiment with different techniques
  
  Evaluation ability and approach
  After completing the course, the student should be able to:
  - report on and present the findings of the project
  - evaluate and assess the project result
  - propose possible improvements

  Prerequisites

  No prerequisites

  Content of the study unit

  The explicit theme can vary from year to year. Examples of possible projects:
  • use machine sound for anomaly detection (“machine hearing”)
  • apply machine vision for intelligent decision making in automation
  • develop and implement a small-scale real-time system
  • set up an industrial communication protocol for process automation

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Satisfactory skills in the project work

  Assessment criteria – good (3-4)

  Good skills in the project work

  Assessment criteria – excellent (5)

  Excellent skills in the project work

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Teaching language

  English

  Timing

  10.04.2023 - 04.06.2023

  Enrollment date range

  01.12.2022 - 12.04.2023

  • 5 credits

  Group(s)

  • AT22HP-V

  Teachers and responsibilities

  Joachim Böling, Ray Pörn

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Automation Technology

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

•   Research methods and scientific writing AT22IS01-3001 29.08.2022-27.12.2022  5 credits  (AT22HP-V) +-
  Competence objectives of the study unit

  Reading and comprehending reliable, scientifically and technically high-level written articles and documentation is a prerequisite for successful work in technologically developing fields. The course introduces the students to the methodology of writing scientific technical documentation and articles. The course also aims at developing student’s written and presentation skills and serve as support for the individual master thesis work.
  
  Knowledge and understanding
  After completing the course, the student should be able to:
  - review and comprehend scientific and technical documentation
  - understand the basic principles of different research methods in engineering
  - understand the principles of writing for academic purposes
  
  Skills and abilities
  After completing the course, the student should be able to:
  - communicate technical and scientific material
  - plan and realize a research study
  
  Evaluation ability and approach
  After completing the course, the student should be able to:
  - perform relevant choices of methodology for a research study,
  - demonstrate their ability to write for an academic audience

  Prerequisites

  No prerequisites

  Content of the study unit

  The contents of the course are reading and summarizing scientific articles and documentation, writing and presentation of technical material and work. Overview of qualitative and quantitative research methods in engineering.

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Satisfactory skills in research methods, scientific writing and presentation skills.

  Assessment criteria – good (3-4)

  Good skills in research methods, scientific writing and presentation skills.

  Assessment criteria – excellent (5)

  Excellent skills in research methods, scientific writing and presentation skills.

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Learning material

  See moodle page

  Learning methods

  Lectures, exercises, discussion and group work

  Objects, timing and methods of assessment

  See moodle page or course info page

  Teaching language

  English

  Timing

  29.08.2022 - 27.12.2022

  Enrollment date range

  15.06.2022 - 11.09.2022

  • 5 credits

  Group(s)

  • AT22HP-V

  Teachers and responsibilities

  Hanna Kuusisto, Ray Pörn

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Automation Technology

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

  Alternative methods of attainment for implementation

  Individual and group assignments or presentations.

  Student's schedule and workload

  A total of 25 h of lectures/exercises and 100 h of own work.

  Assessment criteria

  See moodle page or course info page

  Assessment criteria – satisfactory (1-2)

  See moodle page or course info page

  Assessment criteria – good (3-4)

  See moodle page or course info page

  Assessment criteria – excellent (5)

  See moodle page or course info page

Academic year 2023 - 2024

There is no implementations attached to this degree programme.