Fault Prediction of Online Power Metering Equipment Based on Hierarchical Bayesian Network

Da Cheng, Penghe Zhang, Fan Zhang, Jiayu Huang


The failure rate assessment of online metering equipment is significant for power metering. For traditional methods, the performance of the model is not satisfactory especially in the case of small samples. In this paper, an online power measuring equipment fault evaluation method based on Weibull parameter hierarchical Bayesian model is proposed. Firstly, the z-score method is used to eliminate outliers in the raw failure data. Then, a generalized linear function with variable intercept is established according to the characteristics of failure data. The information of each region is merged using the characteristics of multi-layer Bayesian network uncertainty reasoning. The model parameters are updated based on the Markov chain Monte Carlo method. Thereafter, the trend of failure rate is provided with time-dependent. Finally, the proposed method is verified by the failure samples of the online measurement equipment in three typical environmental areas. The accuracy and validity of the hierarchical Bayesian model is verified by a series of experiments


failure rate; hierarchical Bayesian model; variable intercept; Weibull

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DOI: https://doi.org/10.33180/InfMIDEM2019.205


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