Improving Regime Recognition Model Validation with Labeled Flight Test Data and Visualization Tools

Accurate monitoring of helicopter operational usage relies heavily on robust regime recognition algorithms. How-ever, evaluating these approaches is challenging when they operate as opaque, "black boxes", as in the case of machine learning-based models. This paper introduces a comprehensive evaluation framework designed to assess regime recog-nition models from a number of perspectives and investigate anomalies in the predicted regimes. Centered around a high-fidelity data set derived from scripted flight tests covering a complete usage spectrum, the developed method-ology provides a comparative baseline. The analytical suite includes 3D spatial visualization tools for flight path mapping, sequential anomaly detection, and confusion matrix metrics. While applying the labeled data set to other platforms presents inherent limitations in terms of mapping features and regimes appropriately, the integrated toolset successfully exposes weaknesses in the model and highlights gaps in training data. Ultimately, this evaluation frame-work enhances the interpretability of model outputs and builds confidence in the use of regime recognition algorithms.