Bayesian networks utilization for reliability evaluation of power systems

Ciobanu, Alexandra; Munteanu, Florin; Nemes, Ciprian

doi:10.1109/icepe.2016.7781454

Cited by 6 publications

(1 citation statement)

References 8 publications

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“…However, traditional methods cannot be well suitable to reliability modelling for CNC machine tools due to the small size sample failure data with lots of censored data. For this problem, researchers have proposed many reliability modelling methods based on Bayesian theory, such as Bayesian reliability inference method [20,21], Bayesian modelling method (BMM) [22], and Bayesian networks [23][24][25]. All the methods mentioned above relative to Bayesian theory are subjectively influenced by experts' experience.…”

Section: Introductionmentioning

confidence: 99%

Reliability Modelling of CNC Machine Tools Based on the Improved Maximum Likelihood Estimation Method

Yang

Zhu

Chen

et al. 2018

Mathematical Problems in Engineering

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The existing standard reliability models for computerized numerical control (CNC) machine tools are not satisfactory and they fall short of predicting failure rates or lifetime of key functional parts of CNC machine tools. This is attributed to two reasons: the small sample size of failure data and a large truncated ratio of the censored failure data. Improved correction method (ICM), maximum likelihood estimation (MLE), and empirical maximum likelihood estimation (EMLE) are presented and compared with each other in this study. In order to improve the shortage of reliability models developed by the traditional methods, an improved maximum likelihood estimation method (IMLE) is proposed through enlarging censored failure data. Moreover, the correction factors of mean ratio to extend censored time are designed, by which the censored failure data can be close to the true time between failures (TBF). Furthermore, a solution method of correction factors considering amount of calculation is proposed to meet the requirements of calculation precision. Finally, verification by the orthogonal experiment is simulated to verify the proposed model. The verifying test results show that the proposed method can be applied in reliability modelling for not only CNC machine tools but also the key functional parts of CNC machine tools.

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