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Using big-data and surface fitting to improve aircraft safety through the study of relationships and anomalies

机译：使用大数据和表面拟合来通过研究关系和异常来改善飞机安全

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The aim of this paper is to assess the utility of a Big-Data approach to fault detection for 'systems of systems', utilising the derivation of empirical relationships identified through surface fitting. So-called Big-Data Integrated Vehicle Health Management systems do currently exist, but tend to analyse the health of vehicle systems based on the behaviour of individual sensors and readings. This paper proposes that it is possible to consider vehicle systems with a 'macro' approach and identify relationships between key variables which may not be initially apparent. Used in this paper is the open source flight simulation software FlightGear which has previously been assessed for the development of fault detection systems with positive results. The relationships found can be combined into a model of expected results against which real-time data is tested. Surface fitting and the assessment of 'goodness of fit' is used to identify these relationships. It is proposed that this technique need not be limited to fault detection in vehicle systems but is also applicable to other vital systems which require redundancy and constant health analysis. This paper concludes that this method is a viable approach and that relationships can be successfully identified for fault detection purposes.
机译：本文的目的是评估大数据方法对“系统系统”的故障检测的效用，利用通过表面配件所识别的经验关系的推导。目前存在所谓的大数据集成车辆健康管理系统，但倾向于根据单个传感器和读数的行为分析车辆系统的健康。本文提出可以考虑具有“宏”方法的车辆系统，并识别关键变量之间的关系，这可能不是最初明显的。本文使用的是开源飞行仿真软件飞机精华，先前已被评估为具有积极结果的故障检测系统的开发。找到的关系可以组合成测试实时数据的预期结果模型。表面配件和“健康良好评估”用于识别这些关系。提出，该技术不必限于车辆系统的故障检测，但也适用于需要冗余和恒定健康分析的其他重要系统。本文得出结论，该方法是一种可行的方法，并且可以成功识别这种关系以进行故障检测目的。

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