Developing tools for reconstructing control signals for crash investigations

• Published in: Aircraft design : an international journal 2000-09, Vol.3 (3), p.175-203
• Main Authors: Pispitsos, Stelios P., Napolitano, Marcello R., Seanor, Brad
• Format: Article
• Language: English
• ISSN: 1369-8869
• DOI: 10.1016/S1369-8869(00)00017-3

Following a few aircraft accidents where control surface failures were believed to be a likely cause of the crash, the FAA recently required U.S.-based airlines to retrofit the fleet with newer digital flight data recorders (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 U.S. 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 limited cockpit voice recorder (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. (Author)