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.
Recorded: 10 May 2016
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .Select web site
You can also select a web site from the following list:
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.