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Predictive Maintenance with MATLAB: A Prognostics Case Study

Date: March 9, 2017

Session 1 Time:9:00 a.m. U.S. EST/ 2:00 p.m. GMT/ 3:00 p.m. CET

Session 2 Time:2:00 p.m. U.S. EST/ 7:00 p.m. GMT/ 8:00 p.m. CET

Session 3 Time:7:00 p.m. U.S. EST/ February 15, 2017 11:00 a.m. AEDT; 1:00 p.m. NZDT


Companies that build and operate industrial equipment are storing large amounts of machine data, with the notion that they will be able to extract value from it in the future. However, using this data to build accurate and robust models that can be used for prediction requires a rare combination of equipment expertise and statistical know-how.

In this webinar, we will show how big data and machine learning techniques in MATLAB can be used to estimate equipment health and time until failure. Using data from a real-world example, we will explore importing and pre-processing data, as well as selecting features, and training and comparing multiple machine learning models. We will show how these technologies are used build prognostics algorithms and take them into production, enabling companies to improve the reliability of their equipment and build new predictive maintenance services.

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