Predictive maintenance reduces operational costs for organizations running and manufacturing expensive equipment by predicting failures from sensor data. However, identifying and extracting useful information from sensor data is a process that often requires multiple iterations as well as a deep understanding of the machine and its operating conditions.

In this talk, you will learn how MATLAB^® and Predictive Maintenance Toolbox™ combine machine learning with traditional model-based and signal processing techniques to create hybrid approaches for predicting and isolating failures. You will also see built-in apps for extracting, visualizing, and ranking features from sensor data without writing any code. These features can then be used as condition indicators for fault classification and remaining useful life (RUL) algorithms.

Predictive maintenance algorithms make the greatest impact when they are developed for a fleet of machines and deployed in production systems. This talk will show you how to validate your algorithms, and then integrate them with your embedded devices and enterprise IT/OT platforms.