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 for prediction requires a rare combination of equipment, expertise, and statistical know-how.

In this session, David uses machine learning techniques in MATLAB^® to estimate the remaining useful life of equipment. Using data from a real-world example, the session explores how MATLAB is used to build prognostic algorithms and take them into production, enabling companies to improve the reliability of their equipment and build new predictive maintenance services.