Bayesian Networks Based Testability Prediction of Electronic Equipment

摘要：

The complexity of modern electronic equipment is putting new demand on system testability. Well design for testability (DFT) can save cost in fault detection and isolation, promote efficiency of system maintenance. The primary goal of testability prediction is to analyze and evaluate testability figures of merit (TFOMs) of unit under test (UUT) to support the assessment of the quality of DFT. Bayesian networks (BNs) are the combination of probability theory and graph theory, which has exhibited distinguished performance in representation and reasoning of uncertainty knowledge. So we combine BNs and testability prediction project together. The testability prediction method based on BNs can not only be modeled conveniently, and easy to be integrated into information framework of testability engineering. Predicted result from Bayesian method is more believable than traditional methods.

关键词： testability prediction Bayesian networks diagnosis testability figures of merit.

作者: Wang Baolong Huang Kaoli Xu He Lian Guangyao

作者单位: Beijing Aerospace Control Center Postal Mail Box 5130-111,Beijing 100094,China;Ordnance Engineering Ordnance Engineering College,Shijiazhuang 050003,China Section of PLA Representation in 203 Research Institute,Xian 710065,China

会议类型: 国际会议

会议名称: 2011 10th International Conference on Electronic Measurement & Instruments(第十届电子测量与仪器国际会议 ICEMI2011)

会议地点: 成都

会议语种:英文

页码: 271-273

在线出版日期: 2011-08-16（万方平台首次上网日期，不代表论文的发表时间）