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.