Predictive Maintenance and anomaly detection with MATLAB and AI techniques

This webinar is Part 2 of the Artificial Intelligence in Industrial Automation and Machinery series.

Predictive maintenance has emerged as one of the most important applications driving the digital transformation. However, many organizations are struggling to develop predictive maintenance services. According to many studies, this is most often due to lack of required skills or concerns about data quality.

In this webinar, we will use machine/deep learning techniques in MATLAB to tackle various challenges related to predictive maintenance and anomaly detection. Using data from a real-world example, we will explore importing and pre-processing data, identifying condition indicators, and training predictive models.

In addition to live demonstrations from MathWorks, Danfoss High-Pressure Pumps will share how they got started with condition monitoring using MATLAB, enabling them to improve the reliability of their pumps and build new data-driven services.

Highlights

• Preprocessing sensor data
• Identifying condition indicators
• Machine learning for anomaly detection and diagnostics
• Operationalizing algorithms on embedded systems and IT/OT systems
• Bridging the data gap

About the Presenters

Georg Herborg holds a PhD in Physics from the University of Aarhus within the field of atomics scale studies of metal oxide surfaces. Since then Georg made a career in the Wind Industry with Vestas, taking the role as Director of Tooling, responsible for developing and delivering all wind turbine blade manufacturing equipment. Since 2017 Georg has taken the role as Director of Innovation in Danfoss High Pressure Pumps, accountable for both R&D activities, New Product Developments, Technical ownership of current product portfolio as well as the internal laboratory where products and prototypes are tested and validated. Here Georg has initiated and driven forward a Condition Monitoring Strategy with the clear target to create industry leading tools and insights, that create unique value both for Customers and for Danfoss

Minh Khoa Nguyen works as a Development Engineer in the Innovation department of Danfoss High Pressure Pumps (HPP). He is a Mechatronics Engineer who is specialized in Mathematical modelling and Control systems, and have been with Danfoss HPP for two years and is a part of their Condition Monitoring team, where he automates data analyses. 

Antti Löytynoja joined the MathWorks application engineering team in 2010. 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 specialized in audio signal processing applications, such as sound source localization.

Rainer Mümmler works as a Principal Application Engineer at MathWorks focusing on Data Analysis/Analytics, Artificial Intelligence, Connectivity to Hardware and on solutions for the Internet of Things. Before joining MathWorks he worked as a wind tunnel test engineer and as a freelancer for various Aerospace companies.