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.
潜在问题是影响大型复杂系统安全性、可靠性的重要因素.神经网络是一种新的潜在问题分析方法,但是其分析结果难以解释.本文提出了一种基于电路结构的神经网络模型(Neural network model based on circuit architecture,CArNN),将CArNN作为个体进行集成,形成神经网络集成用于潜在问题分析.对CArNN模型的鲁棒性进行了分析,提出了两个保证模型鲁棒性的约束条件.利用此方法对一个经典电路进行了分析,结果显示,此方法对潜在电路的正确识别率达到94%.
