Predictive Maintenance with MATLAB

Overview
Do you work with operational equipment that collects sensor data? In this webinar, you will learn how you can utilise that data for Predictive Maintenance, the intelligent health monitoring of systems to avoid future equipment failure. Rather than following a traditional maintenance timeline, predictive maintenance schedules are determined by analytic algorithms and data from sensors. With predictive maintenance, organisations can identify issues before equipment fails, pinpoint the root cause of the failure, and schedule maintenance as soon as it's needed.
Highlights
Synthetic data generation using a Simulink model of triplex reciprocating pump model
Development of a multi-class classifier to detect different combinations of faults
How to use the Diagnostic Feature Designer app to extract and analyse features from the fault data
How to use the Classification Learner app to train a machine learning model
How to extract features from pump data and estimate Remaining Useful Life using an exponential degradation model
Fleet of pumps monitoring through an app developed using App Designer and deployed as a web app