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In manufacturing, it's very expensive to do something for equipment or processes after they go wrong. Therefore, it is important to predict problems before they occur and take action to prevent them from happening. To do this, you need to monitor data from the manufacturing site, and if you expect an abnormal situation, you need to check and take necessary actions before the problem occurs. To do this, the following technologies are required. First, an unsupervised learning-based data analysis can be considered to analyze raw data from the field immediately; second, a multivariate analysis is required to analyze many data variables with comprehensive results; and third, an anomaly detection method is required. The paper proposes and validates a method "Multivariate PCA-based Composite Criteria Evaluation Method" for unsupervised learning-based anomaly detection using a combination of Principal Component Analysis algorithm and two kinds of statistical techniques Hotelling T2 control chart and Box-Pierce test. The proposed method has been applied to a product called THiRA Analytics and has been validated in a semiconductor factory and conducted many PoCs for various industries. |
IEEE/ICACT20240049 Slide.04
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