摘要
针对电子电路的故障诊断问题,提出一种基于Bayes决策理论的多传感器数据融合解决方法。通过测试电路中被诊断元件温度和节点电压2个物理量,得出Bayes理论中不同传感器对各待诊元件的先验概率,在此基础上,利用Bayes条件概率公式进行两级数据融合,得到各元件关于故障类型的目标概率值,进而根据最大概率值确定故障元件。Bayes多传感器数据融合诊断与单传感器诊断方式相比,大大提高了故障识别准确率,并降低了故障元件不确定的概率。实验结果证明:该方案是一种有效的电路故障诊断方法。
A data fusion method for circuit fault diagnosis is presented based on Bayes decision theory. By measuring the temperature and voltage of circuit components, the prior probabilities and conditional probabilities of different sensors to every circuit component are obtained. The target probability values of fault types and attributes for the components are calculated via two-level data fusion with Bayes conditioned probability formula, thus the fault component is found according to maximum probability value. Comparing the diagnosis results based on separate sensor to multi-sensor, it is shown that the later not only improves the accurate rate of fault recognition but reduces the probability of uncertainty. Tests indicate that this method is effective for circuit fault diagnosis.
出处
《传感器与微系统》
CSCD
北大核心
2008年第10期33-35,38,共4页
Transducer and Microsystem Technologies
基金
国家"863"计划资助项目(2006AA10A301
2006AA10Z335)
