Data Analytics for Automated Feature Extraction and Anomaly Detection in a Class of Industrial Manufacturing Processes

Industry 4.0/Smart Manufacturing is transforming the manufacturing industry through the integration of technologies such as the Internet of Things (IoT), big data, and cloud computing. These advancements have revolutionized the way production is carried out, leading to the generation of huge amounts of data in a more affordable and accessible manner, which makes it possible for anomalies to be detected. By identifying anomalies in this data, manufacturers can detect potential faults or issues in the production process early. This early detection can help prevent costly downtime, reduce waste, and improve product quality, ultimately leading to better customer satisfaction and profitability. Given the significant potential benefits, we have collected data from a local factory and launched a research initiative to explore how anomaly detection can help small/med-sized manufacturers to adopt Industry 4.0 technologies. This paper addresses this challenge for a key production operation in a local manufacturing plant. In particular, sensors were deployed to continuously measure a critical process variable of the equipment for this operation. Using the sensor data, we develop effective and robust algorithms that can effectively extract the normal pattern features and detect anomalies that occurred during the production process. Numerical experiments demonstrate that the proposed framework can detect the anomalies in high accuracy.