We formulated the anomaly detection problem as a binary classification task on the pressure traces. Experiments known to be affected by air bubbles were labeled as the positive class (class 1), while experiments known to be unaffected-along with additional samples selected via (SNA) from the initial dataset (randomly chosen based on the low expected frequency of air bubble anomalies or guided by model predictions)were labeled as the negative class (class 0).