摘要
电子装备的故障预测是飞机预测与健康管理系统的关键技术。针对电子装备故障预测参数选取和设置的难题,提出了一种故障预测参数选取和设置的新方法。首先基于多信号流图模型,提取反映电子装备故障状态的参数集;通过引入生物统计学中相关危险度,作为统计模型标准,采用数据驱动的方法,选取出最优故障预测参数。通过建立预测参数门限值统计模型趋势图,获得置信度为95%的门限估计值。实例证明通过该方法可快速有效地选取和设置预测参数,同时避免了繁琐的故障模式、故障状态和故障判据的分析,以及主观因素影响,实现预测参数选取和设置的自动化。
Fault prognosis is the key technique of PHM（Prognostic and Health Management）.Aimed at the difficult problems of parameters selection and setting in fault prognosis of electronic equipment,the paper proposes a new method of parameters selection and setting in fault prognosis.Firstly,the parameter set which can reflect fault state of electronic equipment is extracted by using the multi-signal flow graphs model.By introducing the relative risk of biostatistics,the optimized parameters of fault prognosis are selected by using the data-driven method.The prognostic parameter threshold in the confidence level of 95% is got by building statistical model trend graph.The experiment validates that the optimized prognostic parameters can be quickly and effectively selected and set by the proposed method.Simultaneously the complicated analyses of fault mode,fault state,fault diagnosing bases and the influence pf subjective factors are avoided.
出处
《空军工程大学学报（自然科学版）》
CSCD
北大核心
2010年第4期11-15,共5页
Journal of Air Force Engineering University(Natural Science Edition)
基金
国防预研基金资助项目(51317030103)
关键词
故障预测
相关危险度
预测度
多信号流图
fault prognosis
relative risk
prognostic scale
multi-signal flow graph
