A testability growth model based on evidential reasoning with nonlinear optimization

Zonghao, Yang; He, Hong; Li, Tianmei; Xu, Cheng; Yang, Zongxian

doi:10.1109/phm.2017.8079321

Cited by 1 publication

(2 citation statements)

References 14 publications

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“…Over the last several years, many TGMs have been proposed and these models greatly facilitate engineers and managers in tracking and measuring the growth of testability as the system is improved [13]- [20]. Li et al in [13]- [15] found a TGM based on bell-shaped testability growth effort function, in which the main attention is paid on TDL identification.…”

Section: Introductionmentioning

confidence: 99%

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NHPP Testability Growth Model Considering Testability Growth Effort, Rectifying Delay, and Imperfect Correction

Zonghao³

et al. 2020

IEEE Access

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Over the last several years, many testability growth models (TGMs) have been developed to greatly facilitate engineers and managers in tracking and measuring the growth of testability as system is being improved. Most TGMs consider only one or two variation patterns of the aspects, such as the testability growth effort (TGE) in testability design limitation (TDL) identification, the rectifying delay and the new TDL introduction in TDL correction. However, the ignorance of such joint consideration may lead to a lower fitting ability to the fault detection/isolation data. Inspired by the counting idea of nonhomogeneous Poisson process (NHPP), a NHPP based testability growth model (TGM) considering the recurrence rate function (RRF) of TDL identification, TDL correction and new TDL introduction is proposed for the foundation of TGM. A real data set of a missile control system is used to validate the above TGMs in fitting ability, estimation accuracy and prediction capability. Results show that the bell-shaped curve can fit the identification process and rectifying delay process of TDL well, and the imperfect correction of TDL really exists in the testability growth test (TGT), and the inflected s-shaped and Gamma function based TGM gives good capacity to the real data set. INDEX TERMS TGM, NHPP, testability growth effort, rectifying delay, imperfect correction.

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Section: Introductionmentioning

confidence: 99%

“…As a result, the accuracy of the TGM cannot be ensured. Yang et al in [19], [20] proposed a Bayesian testability growth model based on the test data. However, the TGM can only estimate the value of testability,but the prediction of the variation tendency of testability cannot be achieved.…”

Section: Introductionmentioning

confidence: 99%

NHPP Testability Growth Model Considering Testability Growth Effort, Rectifying Delay, and Imperfect Correction

Zonghao³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

A testability growth model based on evidential reasoning with nonlinear optimization

Cited by 1 publication

References 14 publications

NHPP Testability Growth Model Considering Testability Growth Effort, Rectifying Delay, and Imperfect Correction

NHPP Testability Growth Model Considering Testability Growth Effort, Rectifying Delay, and Imperfect Correction

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