Virtual commissioning (VC) is a technique used in the field of automation and control engineering to simulate and test a system's control software and hardware in a virtual environment before it is physically implemented. The aim is to identify and correct any issues or errors in the system before deployment, reducing the risk of downtime, safety hazards, and costly rework.
The virtual commissioning process typically involves creating a digital twin of the system being developed, which is a virtual representation of the system that mirrors its physical behaviour. The digital twin includes all the necessary models of the system's components, such as sensors, actuators, controllers, and interfaces, as well as the control software that will be running on the real system. Once the digital twin is created, it can be tested and optimized in a virtual environment to ensure that it behaves correctly under various conditions.
The benefits of using VC include reduced project costs, shortened development time, improved system quality and reliability, and increased safety for both operators and equipment. By detecting and resolving potential issues in the virtual environment, engineers can avoid costly and time-consuming physical testing and debugging, which can significantly reduce project costs and time to market.
The course includes different modules, each with its own specific role in the process. Together, the modules create a comprehensive virtual commissioning process that makes it possible to test and validate control systems and production processes in a simulated environment before implementing them in the real world.
Modeling and simulation: This module involves creating a virtual model of the system using simulation software. The model includes all the equipment, control systems, and processes involved in the production process.
Control system integration: This module involves integrating the digital twin with the control system, allowing engineers to test and validate the system's performance.
Virtual sensors and actuators: This module involves creating virtual sensors and actuators that mimic the behavior of the physical equipment. This allows engineers to test the control system's response to different scenarios and optimize its performance.
Scenario testing: This module involves simulating different scenarios, such as equipment failures, power outages, or changes in production requirements, to test the system's response.
Data analysis and optimization: This module involves analyzing data from the virtual commissioning process to identify any issues or inefficiencies in the system. Engineers can then optimize the system's performance and ensure that it is safe and reliable.
Describe the use of digital twins for virtual commissioning process.
Develop a simulation model of a production system using a systems perspective and make a plan for data collection and analysis.
Plan different scenarios for the improvement of a production process.
Analyze data from the virtual commissioning process to identify any issues or inefficiencies in the system and then optimize the system's performance.
Needs in the industry
Example battery production: Battery behaviors are changing over time. To innovate at speed and scale, testing and improving real-world battery phenomena throughout its lifecycle is necessary. Virtual commissioning / modeling-based approaches like digital twin can provide us with accurate real-life battery behaviors and properties, improving energy density, charging speed, lifetime performance and battery safety.
Faster innovation (NPI)
Lower physical prototypes
Shorter manufacturing cycle time
Rapid testing of new battery chemistry and materials to reduce physical experiments
Thermal performance and safety
It’s not just about modelling and simulating the product, but also validating processes from start to finish in a single environment for digital continuity.
Suggested target groups
Early career engineers involved in commissioning and simulation projects
Design engineers (to simulate their designs at an early stage in a virtual environment to reduce errors)
New product introduction engineers
Process engineers (mediators between design and commissioning)
In the modern IT world, businesses often have access to large amounts of data collected from customer management systems, web services, customer interaction, etc. The data in itself does not bring value to the business; we must bring meaning to the data to create value. Data mining and machine learning is an area within computer science with the goal of bringing meaning to and learning from data.
This course will focus on applied machine learning, where we learn what algorithms and approaches to apply on different types of data.This course is for experienced developers working in the industry. The course includes the following: Supervised learning, different types of data and data processing, Algorithms for handling text documents, Algorithms for handling data with numerical and categorical attributes, Neural Networks and Deep Learning for image recognition
Would you like to know what Industry 4.0 is about? Then this course is for you!
In the course, we look at enabling technologies of Industry 4.0 from a human and industrial perspective. The course covers many topics and you will learn the basic terminology related to Industry 4.0 as well as insight and understanding of the Fourth Industrial Revolution and how it is set to affect industry and individuals.
Answer Set Programming (ASP) is a declarative programming paradigm designed within the field of Artificial Intelligence (AI), and used to solve complex search-problems. The declarative nature of ASP allows one to encode a problem by means of logic. In this way, unlike in imperative programming approaches, there is no need to design an algorithm as a solution for the given problem. In this sense, ASP is comparable with SAT-based encoding or constraint satisfaction problems. However, due to its stable-model semantics, ASP provides a richer representation language useful to handle uncertain situations more effectively for real world scenarios. The advantages of declarative programming together with non-monotonic nature of ASP in handling uncertainties have recently made ASP more attractive both for academia and industry. This course focuses on formalizing and solving various search problems in planning, scheduling and system configuration in ASP.
Maybe you want to connect monitoring to a truck, or why not build a connected pump control? Do you want to measure temperatures, pressures or vibrations? Do you want knowledge about how to connect one of your existing products? Then this course is for you.
Do you work as an engineer in the industry and want to learn how to develop an idea with IoT? Do you work as a developer at an IT company and want to learn more about the hardware and the entire infrastructure within IoT? The course is primarily aimed at those who are professionals in the engineering profession, but you do not need to be either a programmer or an electronics engineer to take advantage of the course. The content is adapted so that you can work with your specific ideas.
The course is focused on providing both theoretical and practical knowledge in the field of Internet of Things. You will gain knowledge of the area's applications and definitions, and you will learn how to build an IoT device, all the way from hardware to visualization. You will have the opportunity to practically work with hardware, sensors, as well as infrastructure and security. We will work with, among other things, WiFi, BLE, LoRaWAN, SigFox, NB-IoT / LTE-M1, as well as insight into how data is transported throughout from the device to the database and then to the application.
The course will be held mostly at a distance with a couple of scheduled workshops (13/9, 6/10, 27/10) either on site or online. All lectures will be available online. The course will be delivered in a flexible way to facilitate the combination of coursework with your ongoing professional commitments.
You will need to buy IoT hardware before the start of the course, the cost can be different depending on the type of project, guide value is approx: SEK 1,000.
The total scope of the course is normally about 80 hours.Language of instruction: EnglishThe course is free of charge
The purpose of the course “Artificial Intelligence for Managers” is to give managers and decision makers a principle understanding of AI and to increase their understanding of opportunities, difficulties, benefits, and risks connected to AI. It is neither an “Introduction to AI” nor an “AI for dummies” course. Instead, it is set to demystify AI and to transform it into an actionable tool for manages and decision makers.
Target groupThis course is for product managers, project managers, executives, and engineering managers in organizations that have already made, or are about to make, the transition to working with AI.
ContentThe course is organized in three modules. The initial module will focus an introduction to AI, giving an understanding of what type of cases can be addressed with AI and what managers need to know about AI technology. Module two will cover tools and concrete on how to set up an AI strategy and roadmap, how to get started on AI projects, how to integrate AI and IT development, how to (self) evaluate AI in use, and, not to forget, the ethical and legal aspects of AI. The third module will give the participants the chance to use their new knowledge and tools and work with their own practical cases and how they could be addressed using AI.
The goal of the course is to empower the participants to:
Describe the principal concept of AI, its strengths, and shortcomings
Understand opportunities, myths, and pitfalls of AI
Identify problem areas in industry, society, and in management where AI could be utilized
Analyze how AI can be applied in a particular problem area
Manage an AI strategy and get started: implement a strategy and a roadmap to apply AI in a particular problem area
Understand how to integrate AI with IT development
Assess the maturity of AI utilization in an organization
Reflect on applications of AI from an ethical and legal perspective as well as the future challenges (technical, organizational, social, etc.)
Practical informationAll materials will be accessible and include reading material, lecturer slides etc. The lectures can either be attended live via Zoom or later using the recordings at a time that is convenient for the participants. There will be 3 onsite workshops with a focus on interaction with the teacher and the co-participants of sharing real-life experiences and insights. The course will be delivered in a flexible manner to facilitate the combination of course work with your ongoing professional commitments.
The total effort to pass this course is typically around 200 hours.
Teaching language: English
Entry requirementsThe basic eligibility for this course is a bachelor’s degree. Candidates with corresponding work experience are also invited to apply. Two years of relevant work experience is considered equivalent to one year of university studies at bachelor level.
The course is free
The course is part of the programme MAISTR (hh.se/maistr) where participants can take the entire programme or individual courses. The course is for professionals and is held online in English. Application is open as long as there is a possibility of admission. The courses qualify for credits and are free of charge for participants who are citizens of any EU or EEA country, or Switzerland, or are permanent residents in Sweden. More information can be found at antagning.se.
About the course Smart Healthcare with Applications, 4 credits
Who is this course for?The course suits you with any Bachelor’s degree (equivalent of 180 Swedish credit points / ECTS credits at an accredited university) who have an interest in applying Artificial Intelligence (specifically Machine Learning) to healthcare. Leadership/management experience in health-related organization/industry OR a Bachelor degree in computer science is advantageous.
What will you learn from this course?Healthcare as a sector together with other health-related sources of data (municipalities, home sensors, etc.), is now in a place and can take advantage of what data science, Artificial Intelligence (AI), and machine learning (ML) have to offer. Information-driven care has the potential to build smart solutions based on the collected health data in order to achieve a holistic fact-based picture of healthcare, from an individual to system perspective. This course aims to provide a general introduction to information-driven care, challenges, applications, and opportunities. Students will get introduced to artificial intelligence and machine learning in specific, as well as some use cases of information-driven care, and gain practice on how a real-world evidence project within information-driven care is investigated.
What is the format for this course?Instruction type: The lectures, announcements, and assignments of this course will be fully online via a learning management system and presented in English. Each lecture is delivered through a video conference tool with a set of presentation slides displayed online during each class session. Online practical labs (pre-written Python notebooks) are also provided in the lectures.