Stelios P. Pispitsos, Marcello R. Napolitano, Brad Seanor
{"title":"为事故调查开发重建控制信号的工具","authors":"Stelios P. Pispitsos, Marcello R. Napolitano, Brad Seanor","doi":"10.1016/S1369-8869(00)00017-3","DOIUrl":null,"url":null,"abstract":"<div><p><span>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<span> (FDR) and cockpit voice recorder<span> (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 </span></span></span>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 limited 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.</p></div>","PeriodicalId":100070,"journal":{"name":"Aircraft Design","volume":"3 3","pages":"Pages 175-203"},"PeriodicalIF":0.0000,"publicationDate":"2000-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S1369-8869(00)00017-3","citationCount":"0","resultStr":"{\"title\":\"Developing tools for reconstructing control signals for crash investigations\",\"authors\":\"Stelios P. Pispitsos, Marcello R. Napolitano, Brad Seanor\",\"doi\":\"10.1016/S1369-8869(00)00017-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>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<span> (FDR) and cockpit voice recorder<span> (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 </span></span></span>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 limited 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.</p></div>\",\"PeriodicalId\":100070,\"journal\":{\"name\":\"Aircraft Design\",\"volume\":\"3 3\",\"pages\":\"Pages 175-203\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/S1369-8869(00)00017-3\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Aircraft Design\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1369886900000173\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aircraft Design","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1369886900000173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Developing tools for reconstructing control signals for crash investigations
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 limited 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.