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

Authors:
Shunfeng Cheng;Michael Pecht
Affiliations:
Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, United States;Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, United States and Prognostics and Health Management Center, City University of Hong Kong, Hong K ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

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.