基于多特征量的D矩阵模型的建立与分析

针对基于单特征量的D矩阵模型会损失测试信号的大量信息的问题,提出了一种基于多特征量的可测性分析模型。首先,在研究D矩阵模型的基础上,对测试信号进行特征提取和多值编码,建立了基于多特征量的D矩阵模型。其次,基于信息熵制定诊断策略和生成故障诊断树,并对模型进行测试性分析,评估其诊断能力。结果表明,与单特征量的D矩阵模型相比,基于多特征量的D矩阵模型选用的测试点数和模糊组数小,故障隔离率高,诊断测试步骤数少,平均测试费用低。

Establishment and analysis of D matrix model based on multi feature quantity

Aiming at the problem that the D matrix model based on single feature quantity can lose a lot of information about the test signal,a testability analysis model based on multi-feature quantity is proposed.Firstly,based on the study of D matrix model,the feature extraction and multi-value coding of the test signal are carried out,and a D matrix model based on multi-feature quantity is established.Then,the diagnostic strategies are developed based on the information entropy,the fault diagnosis tree is generated,and the model is analyzed to assess its diagnostic capabilities.The results show that the D matrix model based on multi-feature quantity adopts a smaller number of test points and fuzzy group,and has a higher fault isolation rate,less diagnostic test step,and lower average test cost compared with the D matrix model based on the single feature quantity.