國立虎尾科技大學 |

State-Based Analysis of General Aviation Loss of Control Accidents Using Historical Data and Pilots' Perspectives.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	State-Based Analysis of General Aviation Loss of Control Accidents Using Historical Data and Pilots' Perspectives./
Author:	Majumdar, Neelakshi.
Description:	1 online resource (235 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-10, Section: B.
Contained By:	Dissertations Abstracts International84-10B.
Subject:	Aeronautics. -
Online resource:	click for full text (PQDT)
ISBN:	9798379435424

State-Based Analysis of General Aviation Loss of Control Accidents Using Historical Data and Pilots' Perspectives.
Majumdar, Neelakshi.

State-Based Analysis of General Aviation Loss of Control Accidents Using Historical Data and Pilots' Perspectives. - 1 online resource (235 pages)

Source: Dissertations Abstracts International, Volume: 84-10, Section: B.

Thesis (Ph.D.)--Purdue University, 2023.

Includes bibliographical references

General Aviation (GA) encompasses all aircraft operations, excluding scheduled, military, and commercial operations. GA accidents comprise approximately 94% of all aviation accidents in the United States annually. 75% of these accidents involve pilot-related factors (pilot actions or conditions). Inflight loss of control means that the flight crew was unable to maintain control of the aircraft in flight. With almost 50% of loss of control accidents being fatal yearly, it continues to be the deadliest cause of GA accidents.The most common approach to understanding accident causation is analyzing historical data from sources such as the National Transportation Safety Board (NTSB) database. The NTSB database has abundant rich information. In contrast to the extensive investigations into and detailed reports on commercial aviation accidents, GA accident investigations tend to be shorter, and the resulting reports tend to be brief and limited-especially regarding human factors' role in accidents. Only relying on historical data cannot provide a complete understanding of accident causation.There is a clear need to better understand the role of human factors involved in GA accidents to prevent such accidents and thus improve aviation safety. In my research, I focus on a specific type of accidents, inflight loss of control (LOC-I), the deadliest cause of GA accidents. I use historical data analysis and human-subjects research with pilots to investigate the role of human factors in loss of control accidents. Building on previous work, I created a state-based modeling framework that maximizes data extraction and insight formation from the NTSB accident reports by (1) developing a structured modeling language to represent accident causation in the form of states and triggers; (2) populating the language lexicon of states and triggers using insights from accident reports and pilots perspectives via surveys and interviews; and (3) applying Natural Language Processing (NLP) and machine learning techniques to automatically translate accident narratives into the language lexicon. The framework is focused on LOC-I but can be extended to other types of accidents. Findings from my study may help in consistent accident analysis, better accident reporting, and improving training methods and operating procedures for GA pilots.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379435424Subjects--Topical Terms:

678940
Aeronautics.
Index Terms--Genre/Form:

554714
Electronic books.

State-Based Analysis of General Aviation Loss of Control Accidents Using Historical Data and Pilots' Perspectives.
LDR:03626ntm a22003497 4500 001 1145107
005 20240617111346.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379435424
035 $a (MiAaPQ)AAI30499136
035 $a (MiAaPQ)Purdue22677526
035 $a AAI30499136
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Majumdar, Neelakshi. $3 1470322
245 1 0 $a State-Based Analysis of General Aviation Loss of Control Accidents Using Historical Data and Pilots' Perspectives.
264 0 $c 2023
300 $a 1 online resource (235 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-10, Section: B.
500 $a Advisor: Marais, Karen.
502 $a Thesis (Ph.D.)--Purdue University, 2023.
504 $a Includes bibliographical references
520 $a General Aviation (GA) encompasses all aircraft operations, excluding scheduled, military, and commercial operations. GA accidents comprise approximately 94% of all aviation accidents in the United States annually. 75% of these accidents involve pilot-related factors (pilot actions or conditions). Inflight loss of control means that the flight crew was unable to maintain control of the aircraft in flight. With almost 50% of loss of control accidents being fatal yearly, it continues to be the deadliest cause of GA accidents.The most common approach to understanding accident causation is analyzing historical data from sources such as the National Transportation Safety Board (NTSB) database. The NTSB database has abundant rich information. In contrast to the extensive investigations into and detailed reports on commercial aviation accidents, GA accident investigations tend to be shorter, and the resulting reports tend to be brief and limited-especially regarding human factors' role in accidents. Only relying on historical data cannot provide a complete understanding of accident causation.There is a clear need to better understand the role of human factors involved in GA accidents to prevent such accidents and thus improve aviation safety. In my research, I focus on a specific type of accidents, inflight loss of control (LOC-I), the deadliest cause of GA accidents. I use historical data analysis and human-subjects research with pilots to investigate the role of human factors in loss of control accidents. Building on previous work, I created a state-based modeling framework that maximizes data extraction and insight formation from the NTSB accident reports by (1) developing a structured modeling language to represent accident causation in the form of states and triggers; (2) populating the language lexicon of states and triggers using insights from accident reports and pilots perspectives via surveys and interviews; and (3) applying Natural Language Processing (NLP) and machine learning techniques to automatically translate accident narratives into the language lexicon. The framework is focused on LOC-I but can be extended to other types of accidents. Findings from my study may help in consistent accident analysis, better accident reporting, and improving training methods and operating procedures for GA pilots.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Aeronautics. $3 678940
650 4 $a Failure. $3 1365603
650 4 $a Unmanned aerial vehicles. $3 1372845
650 4 $a Causality. $3 591917
650 4 $a Pilots. $3 1470323
650 4 $a Fatigue. $3 582714
650 4 $a Fatalities. $3 1466251
650 4 $a Natural language processing. $3 1050360
650 4 $a Accident investigations. $3 1470324
650 4 $a Aerospace engineering. $3 686400
655 7 $a Electronic books. $2 local $3 554714
690 $a 0800
690 $a 0538
690 $a 0624
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Purdue University. $3 1184550
773 0 $t Dissertations Abstracts International $g 84-10B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30499136 $z click for full text (PQDT)