Using cross-validation for model parameter selection of sequential probability ratio test

The sequential probability ratio test is widely used in in-situ monitoring, anomaly detection, and decision making for electronics, structures, and process controls. However, because model parameters for this method, such as the system disturbance magnitudes, and false and missed alarm probabilities, are selected by users primarily based on experience, the actual false and missed alarm probabilities are typically higher than the requirements of the users. This paper presents a systematic method to select model parameters for the sequential probability ratio test by using a cross-validation technique. The presented method can improve the accuracy of the sequential probability ratio test by reducing the false and missed alarm probabilities caused by improper model parameters. A case study of anomaly detection of resettable fuses is used to demonstrate the application of a cross validation method to select model parameters for the sequential probability ratio test.

► Sequential probability ratio test (SPRT) is widely used in in-situ monitoring, anomaly detection, and decision making.
► Traditionally, model parameters for SPRT are selected by users primarily based on experience.
► Traditionally, the actual false and missed alarm probabilities are typically higher than user’s requirements.
► This paper presents a systematic method to select model parameters for the SPRT by using a cross-validation technique.
► The method can improve the accuracy of SPRT by reducing the false and missed alarm probabilities.