In this paper, we propose a two-stage online data-based diagnostic method that detects issues in the In-Circuit Test (ICT) equipment from a Surface-Mount Technology (SMT) production line. The first stage performs anomaly detection in a univariate stream of test values. The second stage achieves fault detection and isolation based on the process capability and a Gaussian mixture clustering method. Combining the two stages allows improving the online cost of the second stage and improving the confidence and interpretability of the first stage, anomaly detection. Two solutions are compared for the first stage, anomaly detection by Extreme Value Theory (EVT) and a sliding window method. The comparison is done with an automotive, industrial database and shows that using EVT delivers almost the same performance in detection with less computation time.