IEEE Access, Vol. 6, pp. 42566 - 42577, 2018, DOI: 10.1109/ACCESS.2018.2859750

A Joint Distribution-Based Testability Metric Estimation Model for Unreliable Tests

Xuerong Ye1, Cen Chen1, Guofu Zhai1, Myeongsu Kang2, Michael G. Pecht2
1School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, China
2CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA


The selection of tests required to make complex systems testable is a fundamental of system-level fault diagnosis. To evaluate the test selection, testability metric estimation (TME) is required. The influence of unreliable (imperfect) tests, whose outcomes are non-deterministic due to unstable environmental conditions, test equipment errors, and component tolerances, should be considered for accurate TME. Previously, researchers considered a TME model using a Bernoulli distribution with the assumption that the variations of different test outcomes are independent. However, this assumption is not always true. To address the issue, a joint distribution-based TME model was developed derived from the copula function to quantify the influence of dependent outcomes of unreliable tests. The efficacy of the developed TME model was verified with a linear voltage divider and a negative feedback circuit.

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