Learn about statistical and machine learning techniques in MATLAB on real-world datasets to monitor manufacturing processes and detect and identify machine failures.
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Many industries are looking to AI to deliver increased efficiency and improve product quality by automating production process monitoring and maintenance scheduling. Even when production lines and machines are instrumented with sensors as part of digital transformation, engineering teams often lack the specialised skills required by predictive maintenance and advanced process analytics. This webinar will demonstrate statistical and machine learning techniques in MATLAB on real-world datasets to monitor manufacturing processes and detect and identify machine failures. In addition, we will show different techniques to extract diagnostic markers and features from raw sensor data.
Antti Löytynoja joined the MathWorks application engineering team in 2010. Working with MathWorks customers across the Nordic countries, he focuses on MATLAB applications such as data analytics, machine learning, predictive maintenance and application deployment. Prior to joining MathWorks, Antti was a researcher at Tampere University of Technology (TUT), where he also earned his M.Sc. degree in signal processing. At TUT, Antti specialised in audio signal processing applications, such as sound source localisation.
