
Unsupervised Deep Learning Approach for Real-Time Anomaly Detection in Manufacturing Process
This project closes the traditional monitoring gap by developing an unsupervised ML model/algorithm which can detect abnormalities in the industrial processes and notify the user for prompt decision making with high detection rate and minimum false alarm rate to prevent potential major business consequences such as lower production rate or a plant shutdown.