基于模糊关联分类器的民机超限事件诊断方法

现有的民用飞机超限事件智能诊断模型大多属于“黑盒”模型,不利于分析超限事件发生的原因.为此提出了一种基于模糊关联分类器(FAC,Fuzzy Associative Classifier)的民用飞机超限事件诊断方法.该方法抽取发生超限事件时对应的QAR(Quick Access Recorder)参数快照取值,采用模糊C均值(FCM,Fuzzy C-Means)聚类算法对抽取的QAR参数取值模糊预处理,然后基于Apriori算法生成模糊关联分类规则库,并由遗传算法对其进行裁剪,结合模糊分类推理方法形成FAC.采用B737-800实际样本数据进行了验证.实验结果表明,所提出的FAC能有效诊断超限事件,FAC识别超限事件的错误率与最小二乘支持向量机(LS-SVM,Least Squares Support Vector Machine)模型相当,但其解释性方面优于LS-SVM.

Civil aircraft's exceedance event diagnosis method based on fuzzy associative classifier

Most of current civil aircraft's exceedance event intelligent diagnosis models are "black box" model, which donot contribute to analyze the occurrence of civil aircraft's exceedance event. In order to overcome these shortcomings, a civil aircraft's exceedance event diagnosis method based on fuzzy associative classifier (FAC) was proposed. First, quick access recorder's (QAR's) parameters value when exceedance event occured was extracted.Fuzzy C-means (FCM) cluster algorithm was adopted to preprocess extracted QAR's parameters value. Then, the library of fuzzy associative classification rule (FACR) was generated by Apriori algorithm.Genetic algorithm was used to prune the library of FACR.Finally,fuzzy classification reasoning method was integrated to build FAC. The FAC was verified with sample data generated by B737-800. Experiment results show that the FAC can diagnose exceedance event effectively, and its classification error rate is equivalent to least squares support vector machine (LS-SVM), but its interpretability is superior to LS-SVM.