Keynote Lecture: Prof. Diego Galar

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Diego Galar

Industrial Artificial Intelligence - The Driving Force of Predictive Maintenance

Prof. Diego Galar
Professor & Former Director of Academic Innovation and Subsequently
Lulea University of Technology, Sweden
Head, Maintenance and Reliability at Tecnalia, Spain;


Industry and transportation are suffering revolution in which artificial intelligence applications, from virtual assistants to advanced robotics, will disrupt end-to-end value chains amid radical shifts in demand. Advances in AI technologies will enable the industry to leverage rapid growth in the volume of data to optimize processes in real time. They can shorten development cycles, improve engineering efficiency, prevent faults, increase safety by automating risky activities, reduce inventory costs with better supply and demand planning, and increase revenue with better sales lead identification and price optimization.

With the growth of industrial artificial intelligence (AI) all industries are being reimagined in many dimensions indeed one industry that can expect to see unprecedented savings from AI is manufacturing. While most manufacturers are already using some form of preventive or predictive maintenance, AI can usher in a new era of productivity.. Companies are learning how to use their data to not only analyze the past but predict the future as well. Maintenance is a key area that can drive major cost savings and production value around the world. Over the years businesses have overhauled maintenance processes to alleviate downtime and improve effectiveness. There still seems to be confusion, however, around the best way to use data in the quest for optimum operational efficiency.

With AI and machine learning, we have the ability to process massive amounts of sensor data faster than ever before. This gives companies an unprecedented chance to improve upon existing maintenance operations and even add something new: predictive maintenance.

AI is not just helping with failure predictions. It is also supporting operators on the front line to understand their machines even better than before, having all the historical data in one easy-to-access dashboard and keeping everyone at your company on the same page in order to make it easier for machines to get serviced, faster. Now, businesses can ensure that each operator has the right tools and the right knowledge at the right time to get the job done.

However, the great benefit of the AI in industry is the accurate prediction of the performance and the failure in order to replace the traditional preventive maintenance. While effective, there are certain drawbacks to this method. It’s not an exact science, you run the risk of over-maintaining or under-maintaining your assets, and it relies on guidelines for routine checkups but doesn’t consider contextual information. Predictive Maintenance uses condition-based indicators and alerts to surface maintenance needs only when your assets are at risk of breaking down, optimizing your maintenance cadence and maximizing availability.

As connectivity and data accessibility become cheaper and more widespread in industry, many companies are looking to predictive maintenance, or condition-based, maintenance, powered by machine learning and analytics. Most maintenance technologies focus on transporting data, not aggregating it into real-time analytics. But sending the data is just the first step, what you do with that data is what really matters. AI and machine learning can help aggregate and make use of your data, faster.

Predictive maintenance uses data from various sources like historical maintenance records, sensor data from machines, and weather data to determine when a machine will need to be serviced. Leveraging real-time asset data plus historical data, operators can make more informed decisions about when a machine will need a repair. Predictive maintenance takes massive amounts of data and through the use of AI and predictive maintenance software, translates that data into meaningful insights and data points helping you avoid data overload.


Dr. Diego Galar is Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering at LTU, Luleå 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 and also actively involved in national projects with the Swedish industry or funded by Swedish national agencies like Vinnova. He has been involved in the raw materials business of Scandinavia, especially with mining and oil&gas for Sweden and Norway respectively. Indeed, LKAB, Boliden or STATOIL have been partners or funders of projects in the CBM field for specific equipment like loaders, dumpers, rotating equipment, linear assets etc…

He is also principal researcher in Tecnalia (Spain), heading the Maintenance and Reliability research group within the Division of Industry and Transport.

He has authored more than five 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 Católica de Chile. Currently, he is visiting professor in University of Sunderland (UK), University of Maryland (USA), University of Stavanger (NOR) and Chongqing University in China.