This study investigates anomaly detection using a model-based approach, specifically applied to JAXA Low-speed Wind Tunnel (LWT1). A model-based numerical model of the LWT1 was developed, and Uncertainty Quantification (UQ) was conducted to estimate the probability distribution of the objective variable under normal conditions. The 95% confidence interval of the distribution was defined as the normal space for anomaly detection. A demonstration test campaign introducing synthetic anomaly was conducted. The experimental results demonstrate that the model-based approach enables effective anomaly detection even in systems with limited training data for machine-learning.
