​​​Daniil Valme​

Autonomous driving is no longer just an idea of technology vision, instead a real technical trend all over the world. The continuing development to a further level of autonomy requires more from energy optimization. The optimization of electric propulsion drive systems of self-driving electric vehicles by using autonomous and monitoring sensors are not often discussed. The goal of the proposal is to develop a specialized unsupervised prognosis and control platform for such energy system performance estimation. This goal requires the development of several test platforms and digital twins. A digital twin is composed of three components – the physical entities in the real world, their virtual models, and the connected data/view that ties the two worlds together.

Project goals Taking into account the relatively low automation and robotization of the traditional fossil fuels industry, a significant retraining is necessary for the workforce in transition to modern technological industrial sectors. This has to be done, in order for the potentially available workforce to meet the needs and requirements set by the modern mechatronics oriented industry, which actively implements the Industry 4.0 ideology. To meet those requirements, the project proposes retraining courses for the soon-to-be-available workforce. The topics covered by the retraining are electrical drives, automation, robotics, power electronics, and condition monitoring of industrial systems. These separate fields are strongly interconnected and overlapping, and together with the connection point of IT technologies, they can be considered the main technological pillars of modern mechatronics oriented industry.

The objective of the EEV5040 Industrial Automation and Drives activity is to introduce students to the importance of industrial automation and electric drives and the latest trends in these fields (including IoT). As a result of the development activities, students will acquire in-depth knowledge of electric drive management, model-based design methodology and IoT applications in industrial automation in the future. They are able to create, adapt and analyse electric motor control systems and solve real problems in this field. These results influence the quality of industry-specific ICT teaching, providing students with the practical skills and knowledge essential to today's industrial automation. Within the framework of the development project, the subject EEM0040 Machine vision is also amended, where the traditional machine vision curriculum is added to it by adding the hyperspectral technology component. The aim of the development activity is to combine concepts of machine vision with hyperspectral data processing. Within the subject, students will develop practical skills in the use of hyperspectral cameras, from camera setup to processing of various hyperspectral images. They acquire knowledge of the specifics of hyperspectral data, such as the wavelength spectrum and its relationship to the properties of materials. In addition, they learn the methods of machine vision, which allow to identify different objects and characteristics from hyperspectral images.