P‐262: A Data‐Centric Approach to Anomaly Detection for Multivariate Time‐Series Data in Robot Diagnosis System

Abstract

As manufacturing processes become increasingly sophisticated, the abundance of real‐time multivariate data is also increasing. However, the vast majority of data in manufacturing is normal, and abnormal data is scarce, making it difficult to establish correlations between various data sets. While a model‐centric approach holds importance when applying AI in manufacturing, a data‐centric approach, based on domain knowledge of each process equipment, can yield better results. We refer to this approach as engineer's sense. We used the anomaly transformer as an anomaly detection model for time series data, and were able to improve performance through improvements with data centric. The study found that a focus on data‐centric improvement led to higher performance compared to model‐centric improvement. This method identified 88% higher anomaly F1 scores in the evaluation and is expected to be utilized on additional manufacturing equipment in the future. This will result in significant cost and time savings.