A Testability Growth Model based on Evidential Reasoning with Nonlinear Optimization

被引：0
|
作者：
Yang Zonghao [1 ]
He Huafeng [1 ]
Li Tianmei [1 ]
Xu Congqi [2 ]
Yang Zongxian [3 ]
机构：
[1] Xian Inst High Tech, Xian 710025, Shaanxi, Peoples R China
[2] Inst Construct Engn Res, Xian 710032, Shaanxi, Peoples R China
[3] Fudan Univ, Shanghai 201203, Peoples R China
来源：
2017 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-HARBIN) | 2017年
关键词：
Testability Growth; Testability Growth Track; Evidential Reasoning; Nonlinear Optimization; MULTIATTRIBUTE DECISION-ANALYSIS; RULE; UNCERTAINTY;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Testability growth is a process that aims to improve the testability level of the equipment via identifying and removing the testability design defects (TDDs). The establishment of the existing testability growth model (TGM) needs to consider a variety of factors, it's difficult to describe it accurately. To solve this problem, a TGM based on evidential reasoning (ER) method with nonlinear optimization is studied in this paper. According to the growth test data that can achieve the testability growth tracking and predicting. To estimate the parameters of the TGM accurately by using the mean square error (MSE). Finally, growth test data of a stable tracking platform is used to verify the validity of the model. The results show that the tracking accuracy is in the order of 0.0013 magnitude.
引用
收藏
页码：1086 / 1089
页数：4
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