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Developing tools for reconstructing control signals for crash investigations

机译:开发用于重建碰撞调查控制信号的工具

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In recent years, due to the globally increasing trend in air traffic volume, the aviation community has been touched by the occurrence of a number of crashes, although the overall aviation safety is actually improving in most countries. In the US the National Transportation and Safety Board (NTSB) begins its investigation by analyzing the wreckage along with the information from flight data recorder (FDR) and cockpit voice recorder (CVR). In most instances this set of information is enough for the NTSB to discover the cause of the crash, unfortunately, this is not always the case. Until a few years ago FAA regulations mandated the recording of 11-17 flight parameters without specifying the recording of the deflection of primary control surfaces. Following a few accidents where control surface failures were believed to be a likely cause of the crash, the FAA recently required the US-based airlines to retrofit the fleet with newer digital FDRs capable of recording a much larger number of parameters, including, of course, the deflection of primary control surfaces. This rule has a multi-year compliance period. However, some airlines are or have been seeking exemptions from this rule for some specific aircraft soon to be retired from service. Furthermore, only the US commercial fleet is affected by this ruling. Therefore, there is a need for a scheme that can reconstruct additional aircraft time histories to aid investigators fOr crashes with 1imited CVR information and where control surface failure is believed to be a factor. This paper describes a scheme formulated to reconstruct the aircraft primary surface deflection using data available from the current FDRs recording only 11-17 parameters. The scheme consists of two neural networks. The first is used to simulate the aircraft dynamics, while the second is used to reconstruct the primary surface deflections. The methodology is applied to simulated maneuvers from the non-linear model of an F-16 from a commercially available flight simulation software.
机译:近年来,由于全球空中交通量的增长趋势,尽管许多国家的总体航空安全实际上正在改善,但发生了许多坠机事故,使航空界深受感动。在美国,国家运输和安全委员会(NTSB)通过分析残骸以及来自飞行数据记录器(FDR)和座舱语音记录器(CVR)的信息来开始调查。在大多数情况下,这组信息足以使NTSB发现崩溃的原因,不幸的是,并非总是如此。直到几年前,美国联邦航空局(FAA)规定必须记录11-17个飞行参数,而未指定记录主要控制面的挠度。在几次事故中,据信操纵面故障可能是导致坠机的原因,美国联邦航空局最近要求美国的航空公司对机队进行改装,以配备能够记录更多参数的新型数字FDR,当然,其中包括,主要控制面的偏转。该规则有多年的遵守期限。但是,某些航空公司正在或一直在寻求豁免此规则,以使某些特定的飞机即将退役。此外,该裁决仅影响美国商业机队。因此,需要一种方案,该方案可以重建额外的飞机时间历史记录,以帮助调查人员使用带有1个CVR信息的坠机事故,并且认为控制面故障是其中一个因素。本文介绍了一种方案,该方案旨在利用仅记录11-17个参数的当前FDR可获得的数据来重建飞机的主表面偏转。该方案由两个神经网络组成。第一个用于模拟飞机动力学,而第二个用于重建主要表面挠度。该方法适用于来自商用飞行模拟软件的F-16非线性模型的模拟演习。

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