A hybrid deep neural network based on multi-time window convolutional bidirectional LSTM for civil aircraft APU hazard identification-Reference-Cited by-同舟云学术

A hybrid deep neural network based on multi-time window convolutional bidirectional LSTM for civil aircraft APU hazard identification

Published:2022-04 Issue:4 Volume:35 Page:344-361
ISSN:1000-9361
Container-title:Chinese Journal of Aeronautics
language:en
Short-container-title:Chinese Journal of Aeronautics

Author:

ZHOU Di,ZHUANG Xiao,ZUO Hongfu

Funder

National Natural Science Foundation of China

China Scholarship Council

Publisher

Elsevier BV

Subject

Mechanical Engineering,Aerospace Engineering

Reference38 articles.

1. Federal Aviation Administration. Monitor safety/analyze data, Order 8110.107A [Internet]. Oct [cited 2020 November 11]; 2012. Available from: https://www.faa.gov/documentLibrary/media/Order/FAA_Order_8110.107A.pdf.

2. Civil Aviation Administration of China. Requirements for the air operator’s safety management system [Internet]; 2008 Apr [cited 2020 November 11]. Available from: http://www.caac.gov.cn/XXGK/XXGK/GFXWJ/201511/P020151103346689325619.pdf.

3. Federal Aviation Administration. Safety risk management [Internet]. 2018 Apr [cited 2020 November 11]. Available from: https://www.faa.gov/regulations_policies/handbooks_manuals/aviation/risk_management/media/20180427_FAASRMGuidance5StepProcess_signed_508.pdf.

4. International Civil Aviation Organization. Safety Management Manual (SMM) [Internet]. 2012 [cited 2020 November 11]. Available from: https://www.aex.ru/imgupl/files/ICAO%20Doc%209859%20-%20SMM_Edition%204%20-%20Peer%20Review.pdf.

5. Aircraft APU starter health monitoring and failure prognostics [dissertation];Liu,2013

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