基于梯次推理获益原则的故障预测方法

A Failure Prediction Method Based on Echelon Inference and Benefit Principle

针对大数据下产生式规则推理效率低下的缺点以及异构信息融合时知识元权重分配困难的问题,以数据和音频两种异构信息为信息源,设计了基于梯次推理获益原则的产生式规则知识表示方法。根据映射特征向量与非映射特征向量知识元灵敏度的不同,采用分组分级的推理策略;采用互信息-信息增益作为获益尺度,根据知识元特征的获益值确定各知识元的权重。以某飞行数据和同步座舱音频为信源模拟了发动机故障实例并进行故障预测,验证了梯次推理和获益原则的产生式规则表示方法具有较高的执行效率、知识元结构强度和较好的推理效果,可以应用于实际工程的故障诊断和预测。

To solve the problems of poor efficiency of production rule in big data and hard weight distribution in the case of heterogeneous information fusion a knowledge representation method of production rule based on echelon inference and benefit principle was designed taking the data and voice as the information source for fusion.A grouping&ranking inference strategy was proposed on the basis of different sensitivity between mapped knowledge elements and unmapped knowledge elements.The weight of knowledge element was specified by the benefit value obtained from mutual information and information gain.We simulated the engine fault by using the flight data and the synchronous cockpit voice as the information source for making failure prediction.The result shows that our knowledge representation method can provide higher efficiency stronger intensity and more accurate diagnosis result which can be used in practical fault diagnosis and fault prediction.