基于FOQA数据训练的飞机异常检测算法模型和数据模型

Alvin Megatroika, M. Galinium, Adhiguna Mahendra, N. Ruseno
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引用次数: 9

摘要

将两种异常检测模型应用到飞机运行异常检测中。自组织映射神经网络(SOM NN)来源于算法建模的文化,一类支持向量机(SVM)来源于数据建模的文化。研究的目的是发现飞机运行数据或飞行运行质量保证(FOQA)数据中的异常情况,并找出哪个模型表现更好。SOM神经网络发现了69次航班的8800个数据点异常,One-Class SVM发现了651次航班的40392个数据点异常。异常分为性能异常、传感器异常和杂项异常三大类,原因各不相同。结果表明,两种模型都可以检测到FOQA数据中的异常,并且One-Class SVM在发现的异常数量上优于SOM NN,但在运行时间上,SOM NN表现更好,然后根据计算资源可用性得出最佳模型选择,因为SOM NN仍然可以在不消耗昂贵资源的情况下进行改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Aircraft anomaly detection using algorithmic model and data model trained on FOQA data
Two models of anomaly detection are put to use in detecting anomalies in aircraft operation. Self-Organizing Map Neural Network (SOM NN) is used from the culture of algorithmic modeling and One-Class Support Vector Machine (SVM) is used from the culture of data modeling. The goal of the research is to find anomalies within the data of aircraft operation or otherwise known as Flight Operations Quality Assurance (FOQA) data, and to find out which model performs better. SOM NN found 8800 data points of anomalies over 69 flights and One-Class SVM found 40392 data points of anomalies over 651 flights. The anomalies are divided into three categories: performance anomaly, sensor anomaly, and miscellaneous anomaly, each happened because of different causes. It is concluded that both models could detect anomalies within FOQA data and the One-Class SVM outperforms SOM NN in number of anomalies found, however in runtime length, SOM NN performs better, the best choice of model is then concluded according to computing resource availability since SOM NN could still be improved without an expensive resource compared to One-Class SVM.
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