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Terminating the Reliability Growth Test Under Small Sample Failure Dataset
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DOI：10.1109/TR.2025.3623110.png)
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摘要
En 中文
Upon failure discovery, redesign or corrective measures are always implemented to eliminate the defects and improve system reliability in reliability growth management. The Crow-Army Materiel Systems Analysis Activity (AMSAA) model (C-A model) is an effective model candidate in describing cumulative number of faults with respect to reliability growth testing time, in which large amount of recorded failure data are utilized to estimate the parameters in the C-A model. Under small sample size, however, it has been proven difficult to confidently obtain accurate parameter estimators and reliability growth test termination time. In this research, the grey forecasting method, an effective approach in disposing uncertainty especially for scenarios with small sample and poor information, is introduced to fit the limited failure data, predict the next failure time, and extend the original failure dataset. After which, failure dataset is supplemented and parameters in the C-A model are updated. Estimating the reliability metrics at the predicted failure time and comparing with the target requirement, we terminate the reliability growth test, otherwise, continue to predict the failure time with the metabolic grey model. It is shown from two reliability growth cases in the literature that the grey model with first order and one variable (GM(1,1)) possesses a strong data processing ability, makes up for the insufficiencies in circumstance of small sample size, and accelerates the termination time in a reliability growth test.
Keyword:
Reliability
Data models
Predictive models
Analytical models
Forecasting
Maximum likelihood estimation
Estimation
Computational modeling
Weibull distribution
Shape
Crow-Army Materiel Systems Analysis Activity (AMSAA) model (C-A model)
grey system theory (GST)
limited failure data
parameter estimation
reliability growth test (RGT)
期刊
IF:
5.7
论文数:
2.8K
被引数:
8.5K
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