Predictive maintenance is the practice of forecasting equipment failures before they occur and is a high priority for many organizations looking to get business value from historical performance data. New technologies such as machine learning and big data show promising results, but they fail to capture nuances that may be obvious to domain experts familiar with these systems.
In this webinar we will use machine learning techniques in MATLAB to estimate the remaining useful life (RUL) of equipment and isolate the root cause of a failure. Using data from real-world examples, we will use the new Diagnostic Feature Designer app from the Predictive Maintenance Toolbox to explore, visualise, and rank both signal-based and model-based feature extraction approaches. We will then use these features to train and compare multiple machine learning models. We will show how MATLAB is used to build predictive maintenance and condition monitoring algorithms and take them into production, enabling companies to improve the reliability of their equipment and build new predictive maintenance services.