Probability-Based Failure Evaluation for Power Measuring Equipment

Accurate reliability and residual life analysis is paramount during the designing of requirements rotation power measuring equipment (PME). However, sample dataset failure usually sparse contains inevitable pollution data, which has an adverse effect on analysis. To tackle this issue, paper first applies nonlinear regression to fuse rate environmental features PME collected from various locations. Then, a novel binary hierarchical Bayesian probability method proposed model trend identify outliers, in outlier identification structure embedded into Bayesian. Integrating bagging method, with (BHBB) framework further introduced improve predictive performance small by resampling. Last, influence typical features, rate, are obtained BHBB under real multiple Experiments show that our superior interpretability comparing other data-based approaches.