A Novel Testability Optimization Algorithm Counting the Reliability of Test Points

The traditional testability mathematical model is attributed with inaccurate when applied in real industry occasions for it ignores the reliability of the test points (usually considered fully convinced). In this paper, we devise a novel testability optimization algorithm regarding with the reliabil...

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Bibliographic Details
Published inPrognostics and System Health Management Conference pp. 338 - 342
Main Authors Hou, Wenkui, Liu, Liangli, Li, Pengyu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2019
Subjects
Bayes methods
Fault diagnosis
Finite impulse response filters
Genetic-PSO
Greedy algorithm
Mathematical model
Optimization
Testability
Uncertainty
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ISSN2166-5656
DOI10.1109/PHM-Paris.2019.00064

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Summary:The traditional testability mathematical model is attributed with inaccurate when applied in real industry occasions for it ignores the reliability of the test points (usually considered fully convinced). In this paper, we devise a novel testability optimization algorithm regarding with the reliability of test points. First, the D-matrix of uncertainty is acquired based on the Bayes-learning. Then, quantizing the loss function with the information entropy and utilizing the global searching ability of Genetic-PSO algorithm and the efficiency of the Greedy algorithm to form the test group. The proposed algorithm is validated with test data of avionics. The experiment result shows the method is able to select the optimal test group considering the uncertainty.
ISSN:2166-5656
DOI:10.1109/PHM-Paris.2019.00064