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 video, 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.
Get in touch with us: events@techsource-asia.com
View Our Upcoming Events: hubs.ly/H0WQGB40
View Training Updates: hubs.ly/H0WQJ1H0
------------------------------------------------------------------------------------------------------------------------
Subscribes for more updates:
Facebook: hubs.ly/H0VRW460
LinkedIn : hubs.ly/H0VRVgl0
RU-vid: hubs.ly/H0VRWl80
20 авг 2024
