Multivariate PCA-Based Composite Criteria Evaluation Method for Anomaly Detection in Manufacturing Data

In recent years, manufacturing sites have become more intelligent and efficient by adopting various IT technologies. Among them, equipment and process abnormality detection is a topic of high interest for efficient factory operation. In this paper, we propose a method that can detect comprehensive abnormalities by utilizing the PCA algorithm, which is an unsupervised learning-based data analysis method that can easily analyze multivariate data and detect abnormalities in the data, the Hotelling T² method, which is suitable for multivariate data analysis, and the Box-Pierce statistical method to increase the detection criteria of abnormality detection data. To verify the effectiveness of the proposed method, experiments were conducted and validated using a chemical product production dataset. We expect that this method can be utilized for equipment and process anomaly detection in real-time at manufacturing sites.