Keynote Lecture: Prof. Diego Galar

by — last modified Nov 21, 2017 06:52 PM

Diego Galar

Data Science in Industry and Transport: The black swan effect and the swan song desire

Prof. Diego Galar
Professor & Former Director of Academic Innovation and Subsequently
Pro-Vice-Chancellor; Division of O&M Engineering
Lulea University of Technology, Sweden
Head, Maintenance and Reliability at TECNALIA, Spain;
VOLVO Chair for Reliability and Maintenance, Skovde University, Sweden


Industrial systems are complex with respect to technology and operations. For the operations and control of such complex environments, a viable solution is to apply intelligent computerized systems. Industry 4.0 is a term that describes the fourth generation of industrial activity which is enabled by smart systems and Internet-based solutions. Prediction in general and prognosis of asset condition in particular are the main application areas of this revolution, referred to as maintenance 4.0, in form of self-learning and smart systems that predicts failure. Indeed, all the efforts are focused on the search for the swan song of the assets in the way of final gesture, effort, or performance given just before shutdown or unexpected stoppage.

Thus, for complex assets, much information needs to be captured and mined to assess the overall condition of the whole system. Therefore the integration of asset information is required to get an accurate health assessment of the whole system, and determine the probability of a shutdown or slowdown. Moreover, the data collected are not only huge but often dispersed across independent systems that are difficult to access, fuse and mine due to disparate nature and granularity. If the data from these independent systems are combined into a common correlated data source, this new set of information could add value to the individual data sources by the means of data mining.

However, the data collected are not sufficient due to the black swan effect in industry which pop up by the means of rare events not considered by the data driven models due to low latency events. The black swan events is a metaphor that describes an event that comes as a surprise, has a major effect, and is often inappropriately rationalized after the fact with the benefit of hindsight.

This talk will address the issues and challenges of data science in industry and transport emphasizing the positive effects for swan song identification and detection considering its limitations if black swans are neglected.


Dr. Diego Galar is Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering at LTU, Lulea University of Technology where he is coordinating several H2020 projects related to different aspects of cyber physical systems, Industry 4.0, IoT or industrial Big Data. He was also involved in the SKF UTC centre located in Lulea focused on SMART bearings. He is actively involved in national projects with the Swedish industry and also funded by Swedish national agencies like Vinnova.

He is principal researcher in Tecnalia (Spain), heading the Maintenance and Reliability research group with the Division of Industry and Transport. He has authored more than four hundred journal and conference papers, books and technical reports in the field of maintenance, working also as member of editorial boards, scientific committees and chairing international journals and conferences and actively participating in national and international committees for standardization and R&D in the topics of reliability and maintenance.

In the international arena, he has been visiting Professor in the Polytechnic of Braganza (Portugal), University of Valencia and NIU (USA) and the Universidad Pontificia Catolica de Chile. He has been a visiting professor in University of Sunderland (UK), University of Maryland (USA), and Chongqing University in China.