전체메뉴
Article Search

KJASEM The Korean Journal of Aerospace and Environmental Medicine

QR Code QR Code

Article Tools

Article

Review Article

항공 피로위험관리를 위한 생체수리적 모델 접근 방안

A Study on the Biomathematical Model Approach for Aviation Fatigue Risk Management

김대호

공군 항공안전단

Daeho Kim , Ph.D.

Republic of Korea Air Force Aviation Safety Agency, Seoul, Korea

Correspondence to:
Daeho Kim
Republic of Korea Air Force Aviation Safety Agency, 77 Yeouidaebang-ro 22-gil, Dongjakgu, Seoul 07056, Korea
Tel: +82-2-827-7735
Fax: +82-2-827-7735
E-mail: daehoda@hanmail.net
Received: March 4, 2022; Accepted: March 22, 2022

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Korean J Aerosp Environ Med 2022; 32(1): 4-12

Published April 30, 2022 https://doi.org/10.46246/KJAsEM.220004

Copyright © Aerospace Medical Association of Korea.

Abstract

Go to
In the aviation sector, it is recommended to adopt an aviation safety management system (SMS) from international organizations (International Civil Aviation Organization, Federal Aviation Administration, etc.) and to apply a related system in each organization. Among them, fatigue management recommends fatigue risk management system (FRMS) operating as part of SMS proactive risk management. Advanced aviation organizations are developing and applying various related risk assessment techniques that consider characteristics in order to apply scientific and systematic FRMS. Among which the biomathematical fatigue model (BFM) are representative. The Bio-mathematical Model is designed to represent the level by converting it into a simple numerical score, taking into account various related factors for the measurement object. The BFM is tool to predict the level of fatigue of the crew based on scientific understanding of the factors that contribute to fatigue. The Biomathematical Model is used as a scientific approach that promotes the transition to a performance-based safety management. In this study, the recent trends and implications for the BFM developed and applied in the aviation field are to be reviewed. First, FRMS was considered within the SMS framework, then the characteristics and application methods of the BFM were examined, and finally, the direction of the development of the BFM was suggested.

Keywords

Fatigue, Risk, Safety, Model

References

Go to
  1. Craig PA. The killing zone: how & why pilots die. New York (NY): McGraw Hill; 2001.
  2. ICAO. Annex 19: safety management [Internet]. Montréal (QC): ICAO; 2016 [cited 2022 Mar 3]. Available from: https://www.icao.int/safety/SafetyManagement/WebsiteDesignJuly2016/Flyer_US-Letter_ANB-ANNEX19-SM_2016-10-03.AP.pdf.
  3. ICAO. Annex 6 - operation of aircraft. Part 1 [Internet]. Montréal (QC): ICAO; 2011 [cited 2022 Mar 3]. Available from: https://www.icao.int/safety/fatiguemanagement/Fatigue%20Management%20Docs/FM_Annex%206%20Pt1.pdf.
  4. Federal Aviation Administration. AC 120-103A - fatigue risk management systems for aviation safety. Washington, D.C.: Federal Aviation Administration; 2013.
  5. ICAO. ICAO safety management manual doc 9859. Montréal (QC): ICAO; 2018.
  6. Civil Aviation Safety Authority. Biomathematical fatigue models guidance document. Albert Park: Civil Aviation Safety Authority; 2014.
  7. Borbély AA. A two process model of sleep regulation. Hum Neurobiol 1982;1:195-204.
  8. ICAO. Fatigue risk management systems: manual for regulators. Montréal (QC): ICAO; 2012.
  9. IATA, IFALPA. Fatigue risk management systems: implementation guide for operators. Montréal (QC): IATA; 2011.
  10. Kim D. How critical is the fatigue management for pilots in aviation safety? - a review, an investigation and some critical needs. Korean J Aerosp Environ Med 2013;23:31-38.
    CrossRef
  11. Kim D. A study on the pilot fatigue measurement methods for fatigue risk management. Korean J Aerosp Environ Med 2020;30:54-60. https://doi.org/10.46246/KJAsEM.30.2.54
    CrossRef
  12. Spencer MB, Robertson KA, Folkard S. The development of a fatigue/risk index for shiftworkers. Research report 446. Bootle: Health and Safety Executive; 2006.
  13. Jung W, Ko JH, Park J, Kwak SL, Lim SS. Task load analysis of KTX operation by using NASA-TLX method. Proc Korean Soc Railw 2006:1082-1087.
  14. IATA. Common protocol for minimum data collection variables in aviation operations. Montréal (QC): IATA; 2015.
  15. Roach GD, Fletcher A, Dawson D. A model to predict workrelated fatigue based on hours of work. Aviat Space Environ Med 2004;75(3 Suppl):A61-A69; discussion A70-A74.
  16. U.S. Air Force. MACI 90-903, aviation operational risk management (AvORM) program. Washington D.C.: U.S. Air Force; 2018.
  17. Kim DH, Lee JR. A study on application of fatigue risk management system for pilot to fly longer hours. J Korean Soc Aviat Aeronaut 2019;27:47-53. https://doi.org/10.12985/ksaa.2019.27.2.047
    CrossRef
  18. Dey A, Mann DD. Sensitivity and diagnosticity of NASATLX and simplified SWAT to assess the mental workload associated with operating an agricultural sprayer. Ergonomics 2010;53:848-857. https://doi.org/10.1080/00140139.2010.489960
    Pubmed CrossRef
  19. InterDynamics. FAID quantum user guide [Internet]. Brisbane: InterDynamics [cited 2022 Mar 3]. Available from: http://www.interdynamics.com.
  20. U.S. Air Force. Air force instruction 90-802. Washington D.C.: U.S. Air Force; 2013.
  21. Miller JC, Fisher SD, Cardenas CM. Air force shift worker fatigue survey. Brooks City-Base (TX): Human Effectiveness Directorate Biosciences and Protection Division Fatigue Countermeasures Branch; 2005 Aug. Report No.: AFRL-HEBR-TR-2005-0128.
    CrossRef
  22. Hursh SR, Redmond DP, Johnson ML, Thorne DR, Belenky G, Balkin TJ, et al. Fatigue models for applied research in warfighting. Aviat Space Environ Med 2004;75(3 Suppl):A44-A53; discussion A54-A60.
  23. Ancoli-Israel S, Cole R, Alessi C, Chambers M, Moorcroft W, Pollak CP. The role of actigraphy in the study of sleep and circadian rhythms. Sleep 2003;26:342-392. https://doi.org/10.1093/sleep/26.3.342
    Pubmed CrossRef

Related articles in KJASEM

More Related Articles