Niclas Björsell received his B.Sc. in Electrical Engineering and his Lic. Ph. in Automatic control from Uppsala University, Sweden in 1994 and 1998, respectively; he received his Ph. D. in Telecommunication from the Royal Institute of Technology, Stockholm, Sweden, in 2007. In 2012 he was appointed Docent in Telecommunications at the Royal Institute of Technology, Stockholm. He has been full Professor since 2021.
For more than 25 years he has hold positions in the academy as well as in industry. He has almost 20 years of experience from research and development projects; both national and international and mainly in collaborations between industry and the academy. He is currently Associate Professor at the University of Gävle and program director for the study program in Automation Engineering. He has published more than 80 papers in international peer-review journals and conferences, and his research interests include radio frequency measurement technology, analog-to-digital conversion, non-linear systems, wireless communication and automation.
Currently, Niclas Björsell is involved in two research projects, one within wireless communication for automation systems and one within medical measurement technology.
Predictive Maintenance from a System Perspective
Effective maintenance is critical to most operations; maintenance activities extend equipment lifetime, improve
reliability, and prevent deterioration. Thereby it is defined to be one of inner most circles in a circular economy.
Industrial maintenance is basically reactive, corrective and preventive, not taking into consideration the huge amount
of data being generated on the shop floor, nor the available new digital technologies that have emerged in recent
years. To be proactive and predictive, maintenance strategies can take advantage of emerging digitalized
technologies such as advanced data analysis, machine learning, big data and cloud computing to collect, store and
analyze the available data.
The project focuses on digitalized maintenance in the process industry in general and the steel industry in particular.
These industries differ, to some extent, from the manufacturing industry, since it is common to have production units
that gradually and continuously process raw materials through various interconnected sub-systems into the final
products. Optimizing maintenance for an individual sub-system does not necessarily mean the optimum for a
continuous process. In process industry a stop in a sub-system often results in a stop of the entire process chain. The
project adds a new way of thinking with a systems perspective that is extra important in process industries.
Although analyzes of data are important, they will not be useful unless analyzes can be fed back to operators and
decision-makers to make the right decision about action. Visualizations in 2D and 3D have been suggested previously
as powerful tools in intelligent maintenance. Albeit, the system of system perspective means an increased complexity
not least for those who have to make decisions based on the data generated. Due to the complexity of the data
collected from the process and due to the intertwined nature of the involved sub-systems it is crucial that the data is
visualized in an efficient way that enables comprehension by for operators and maintenance personnel enabling them
to diagnose the status of the system and to make well-informed decisions.
This project involves three leading steel companies OVAKO, Sandvik SMT and SSAB together with ABB and the
University of Gävle (HiG). By jointly developing and evaluating methods for predictive maintenance and visual
decision support as well as implementing it on industrial control and monitoring systems, conditions are created for
predictive maintenance of cooperating machines.
Innovative Digital Maintenace
The digitization of industry creates new opportunities for SMEs in several different areas. This project intends to focus on the possibilities for more efficient maintenance in existing production, new services for maintenance and maintenance planning, added value for products and to create an attractive workplace for a widened staff group. The overarching goal is that above all small and medium-sized companies within the region should increase competitiveness and employment. In the long term, it leads to positive effects on human capital, innovativeness, economic profitability, circular economy and regional attractiveness. The project begins with an inventory in order to identify where the company is in the digitization process today and where it wants to be in the future. Four work packages are then carried out: (i) An activity based on participant-driven competence development, (ii) using machine learning to produce digital twins which can be used for predictive maintenance. (iii) Predictive maintenance, where we estimate the remaining service life and thus the need for maintenance. (iv) The final work package consists of communicating results to an operator and/or maintenance planner. Within the project, a number of case studies will be carried out at participating SME companies.
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