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Fault Detection of Aircraft System with Random Forest Algorithm and Similarity Measure

机译：随机林算法的飞机系统故障检测及相似度测量

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Research on fault detection algorithm was developed with the similarity measure and random forest algorithm. The organized algorithm was applied to unmanned aircraft vehicle (UAV) that was readied by us. Similarity measure was designed by the help of distance information, and its usefulness was also verified by proof. Fault decision was carried out by calculation of weighted similarity measure. Twelve available coefficients among healthy and faulty status data group were used to determine the decision. Similarity measure weighting was done and obtained through random forest algorithm (RFA); RF provides data priority. In order to get a fast response of decision, a limited number of coefficients was also considered. Relation of detection rate and amount of feature data were analyzed and illustrated. By repeated trial of similarity calculation, useful data amount was obtained.
机译：具有相似度测量和随机林算法的故障检测算法研究。组织算法应用于我们被美国阅读的无人驾驶飞机（UAV）。相似度测量是通过距离信息的帮助来设计的，并且还通过证明验证其有用性。通过计算加权相似度测量来执行故障决定。使用健康和故障状态数据组之间的12个可用系数来确定决定。相似度测量加权并通过随机林算法（RFA）获得; RF提供数据优先级。为了获得决定的快速响应，还考虑了有限数量的系数。分析和说明了检测率和特征数据量的关系。通过重复试验相似性计算，获得有用的数据量。

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