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An analysis of flight Quick Access Recorder (QAR) data and its applications in preventing landing incidents

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  • Wang, Lei
  • Wu, Changxu
  • Sun, Ruishan

Abstract

A long landing is one type of flight incident that will multiply the risk of a runway excursion. It occurs frequently but receives little attention in research due to difficulty in obtaining the real flight data. The aim of this paper is to discover key flight parameter features of long landing incidents by analyzing Quick Access Recorder (QAR) data and put forward prevention measures from the perspective of pilot operation at the same time. First, 73 flight performance parameter variables and 4 operation parameter variables were defined, covering major landing stages from 1500ft to touchdown. Then 128 cases of selected QAR data were divided into two groups according to the threshold of identifying normal and long landing. Second, each flight parameter variable of these 128 flights was compared between groups and then the logistic and linear regression models were developed respectively to further examine the links between touchdown distance and these flight parameter variables. Third, potential flight operation causing performance difference of long landing incidents was also analyzed. Finally results indicate that the period of 200ft to touchdown is the key stage of landing and flare is the most critical operation affecting touchdown distance. It is suggested that the pilot should inspect the ratio of descent rate and groundspeed carefully at the height of 50ft and pilot׳s faster and steady pulling up columns is probably helpful for an excellent flare and landing. The findings are expected to be applied into flight operation practice for further preventing long landing incidents and even the runway excursion accidents.

Suggested Citation

  • Wang, Lei & Wu, Changxu & Sun, Ruishan, 2014. "An analysis of flight Quick Access Recorder (QAR) data and its applications in preventing landing incidents," Reliability Engineering and System Safety, Elsevier, vol. 127(C), pages 86-96.
    • Handle: RePEc:eee:reensy:v:127:y:2014:i:c:p:86-96
    DOI: 10.1016/j.ress.2014.03.013
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    References listed on IDEAS

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    1. Kirkland, I. & Caves, R.E. & Hirst, M. & Pitfield, D.E., 2003. "The normalisation of aircraft overrun accident data," Journal of Air Transport Management, Elsevier, vol. 9(6), pages 333-341.
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    Cited by:

    1. Guo, Kai & Ye, Zhisheng & Liu, Datong & Peng, Xiyuan, 2021. "UAV flight control sensing enhancement with a data-driven adaptive fusion model," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    2. Mark W Wiggins & Jaime Auton & Piers Bayl-Smith & Ann Carrigan, 2020. "Optimising the future of technology in organisations: A human factors perspective," Australian Journal of Management, Australian School of Business, vol. 45(3), pages 449-467, August.
    3. Jia-Qi, Liu & Yun-Wen, Feng & Da, Teng & Jun-Yu, Chen & Cheng, Lu, 2023. "Operational reliability evaluation and analysis framework of civil aircraft complex system based on intelligent extremum machine learning model," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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