Companies that make 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 use machine learning techniques in MATLAB to estimate remaining useful life of equipment. Using data from a real world example, we will explore importing, pre-processing, and labeling data, as well as selecting features, and training and comparing multiple machine learning models.  We will show how MATLAB is used to build prognostics algorithms and take them into production, enabling companies to improve the reliability of their equipment and build new predictive maintenance services.