Intelligent Industrial Processes & Enabling ICT – A Machine Learning and Intelligence Perspective

• Main Authors: Sandin, Fredrik, Gustafsson, Lennart
• Format: Report
• Language: English
• Published: Luleå tekniska universitet, EISLAB 2014
• Subjects: Other Electrical Engineering; Electronic Engineering; Information Engineering; Annan elektroteknik och elektronik; Luleå
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-23813

Intelligent Industrial Processes (IIP) and Enabling Information and Communication Technology (Enabling ICT) are two out of the nine areas of excellence in research and innovation at the Luleå University of Technology (LTU), which are formed to foster interdisciplinary research and innovation in strategically important areas. This report presents a perspective on the role of machine learning and intelligence in these two areas, focusing in particular on future ICT for industrial process automation (ProcessIT) up to the year 2030. The study that is presented here complements similar studies made in other fields, with the common goal to create the first inputs for a broader discussion and formulation of strategic objectives in the form of a roadmap.This report presents my interpretation of the concept of Intelligent Industrial Processes and the role of ICT in that context, including novel information processing methods and devices that are inspired by biological circuits and systems. This report also includes brief introductions and definitions of important concepts; a summary of seven documents presenting international strategic agendas and objectives; a summary of identified strengths, weaknesses, opportunities and threats; a description of selected research trends with references to interesting results; a tentative outline of interesting research problems and first steps towards 2030; and a list of research groups with complementary competences that may merit future partnership. It is concluded that the Open Research and Innovation Platform that is outlined in a parallel study would be a valuable resource for machine-learning research, development and education because transparent access to data is a key enabling factor. In terms of machine-learning research it is concluded that we need to take the step from studies of isolated learning algorithms and applications to closed-loop learning architectures for large-scale sensor-actuator systems, possibly including human- machine interaction, decision support systems ...