D-statistic contribution analysis has been frequently used in practice for fault diagnosis.Existing algorithms for computing contributions to D-statistic tend to distribute cross-term contribution equally between two ...D-statistic contribution analysis has been frequently used in practice for fault diagnosis.Existing algorithms for computing contributions to D-statistic tend to distribute cross-term contribution equally between two correlated variables.This leads to increased variance in contribution estimation and hence poor separability of faulty and normal variables.A new method for contribution calculation to D-statistic is proposed here which introduces a weighting scheme capable of distinguishing the contributions of two correlated variables.Simulation examples show that the proposed approach achieves improved resolution for distinguishing faulty and normal conditions.展开更多
基金the National Basic Research Program (973) of China(No.2010CB731800)the National Natural Science Foundation of China(Nos.60974059, 60736026 and 61021063)
文摘D-statistic contribution analysis has been frequently used in practice for fault diagnosis.Existing algorithms for computing contributions to D-statistic tend to distribute cross-term contribution equally between two correlated variables.This leads to increased variance in contribution estimation and hence poor separability of faulty and normal variables.A new method for contribution calculation to D-statistic is proposed here which introduces a weighting scheme capable of distinguishing the contributions of two correlated variables.Simulation examples show that the proposed approach achieves improved resolution for distinguishing faulty and normal conditions.
