Machine Health in a Click: A Website for Real-Time Machine Condition Monitoring

Abstract

Globalization in the current digital era has made it easier to use information technology to obtain fast and accurate information. One source of information is a website that can be used to monitor machine conditions in the industry. A good machine maintenance strategy is needed to maintain and increase machine productivity. Therefore, this research aims to build a website to monitor machine conditions in real-time. The machine condition is monitored using sushi sensors to track parameters such as temperature, acceleration, and velocity. Deep learning analysis is then used to identify anomalies in the machine. Using the SCRUM method, this website was successfully built. From the results of tests carried out using unit testing and integrated testing, every feature on this website can run well and according to user needs.