Abstract
Accurate reliability and residual life analysis is paramount during the designing of reliability requirements and rotation of power measuring equipment (PME). However, the sample dataset of failure is usually sparse and contains inevitable pollution data, which has an adverse effect on the reliability analysis. To tackle this issue, this paper first applies nonlinear regression to fuse the failure rate and environmental features of PME collected from various locations. Then, a novel binary hierarchical Bayesian probability method is proposed to model the failure trend and identify outliers, in which the outlier identification structure is embedded into hierarchical Bayesian. Integrating binary hierarchical Bayesian and the bagging method, a binary hierarchical Bayesian with bagging (BHBB) framework is further introduced to improve predictive performance in a small sample dataset by resampling. Last, the influence of typical environmental features, failure rate, and reliability are obtained by the BHBB under the real sample dataset from multiple typical locations. Experiments show that our framework has superior performance and interpretability comparing with other typical data-based approaches.
Highlights
We propose the binary hierarchical Bayesian with bagging, and binary
widely applicable information criterion (WAIC) of binary hierarchical Bayesian with bagging (BHBB) is lower than Hierarchical Bayesian (HB) and the value wr is higher than HB indicating the BHBB
The inaccurate prediction results of failure rate and reliability in a small sample dataset with outliers have been addressed. This method overcomes the sole objective evaluation of failure rate, where multisource information is embedded in BHBB
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Some other acceleration methods can be found in the literature [13] This reliability model is difficult to predict with actual environmental operational data. To avoid isolation of features and models, different Bayesian models have been proposed to make evaluations and predicts of failure rate. A wide confidence interval and volatility forecast results will be provided To resolve these difficulties and limitations mentioned above, a method for identifying outliers and being able to fuse multiple data is required. Combined with the bagging method, a binary hierarchical Bayesian model with bagging (BHBB) is further proposed to reduce the failure prediction variance of the PME. The effectiveness of the proposed method is tested and verified with actual sample data of electricity meters from three typical locations.
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