Automatic identification of wake vortex traverse by transport aircraft using fuzzy logic

Al-Mahadin, Aziz; Bouslama, Faouzi

doi:10.1109/iscmi.2017.8279613

Cited by 5 publications

(7 citation statements)

References 18 publications

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“…In this research, a number of criteria were established to support the systematic analysis of flight data to identify flight events with higher accuracy. Results from this investigation and other research (Al-Mahadin, & Bouslama, 2017, 2019a, 2019b can aid the adjustment of airplane vortex separations which can reduce hazardous vortex encounters and enhance the capacity of various busy airports. Recognition of the investigated criteria by airline pilots will improve their reporting skills and increase the identification accuracy of the various flight events.…”

Section: Discussionmentioning

confidence: 75%

“…The developed criteria may aid the establishment of automatic identification of various flight events utilizing artificial intelligence techniques (Al-Mahadin, & Bouslama, 2017, 2019a, 2019b. The identification of vortex encounters from this investigation can be utilized to study the recognition of other flight events including hard landing, wind shear, turbulence, etc.…”

Section: Discussionmentioning

confidence: 99%

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Developed Criteria to Improve Pilot Reporting of Airplane Vortex Encounters

Al-Mahadin

Dalkılıç

2020

Aviation

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Leading airplane vortices can be hazardous to following airplanes. The regulated minimum separations between following and leading airplanes are sometimes overjudged, hence causing reduction in the capacity of airports. In other instances, they are underjudged and subsequently causing airplane incidences. A vital contribution to the establishment and adjustment of vortex-related minimum airplane separations rely on the identification of vortex encounters through pilot reporting with a manual analysis of flight data from FDRs (flight data recorders). This current process relies on judgment of both the pilot and the airline analysist. Hence, it is subjective and sometimes lacks the required accuracy. Therefore, it is desirable to set a number of criteria, which can be utilized to evaluate the accuracy of wake vortex encounter identification. These criteria can save time, and are both accurate and simple. This study investigates 54 pilot reports of flight events to establish a set of criteria that enable concerned aviation organizations to confirm airplane vortex encounters with higher accuracy. This also helps airlines and aviation stakeholders to introduce new regulations and enhancements such as pilots and FDR analysts training on vortex identification. Such measures will enhance safety, improve aviation operation efficiency and allow revision of vortex-separation regulations.

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Section: Discussionmentioning

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Developed Criteria to Improve Pilot Reporting of Airplane Vortex Encounters

Al-Mahadin

Dalkılıç

2020

Aviation

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“…Recent studies on the automatic identification of aircraft vortex encounters have shown a great potential in using soft-computing approaches. In [1], the authors used fuzzy logic (FL) to model and identify vortex encounters. FL tolerates data imprecision and cope well with complexities in modeling the vortex encounters.…”

Section: Introductionmentioning

confidence: 99%

“…The authors continue to investigate machine learning by using various Manuscript architectures of pure neural networks (NNs). These NNs are constructed based on a similar reduced set of parameters as in [1]- [3]. The paper is structured as follows.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Airplane Wing-Tip Vortices Encounters Using Neural Networks

Al-Mahadin¹,

Bouslama²

2019

IJMLC

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In earlier papers, the authors identified critical parameters to be used in any effective identification of aircraft vortex encounters. Various techniques of pure fuzzy logic and hybrid soft-computing approaches were used to model and successfully classify vortex encounters. In this paper, the authors consider pure neural networks models having different architectures to identify aircraft encounters of wing-tip vortices. The automatic identification of airplane vortex encounters using neural networks gives excellent accuracy when compared with manual approaches. The highest accuracies are obtained by probabilistic neural networks. They are about 93%, 73% and 83% for the overall training, the overall testing and the overall average, respectively. The achieved results confirm the effectiveness of some neural network techniques and the choice of the critical parameters to automatically identify wing-tip vortices.Index Terms-Wing tip vortices, vortex encounters, neural networks (NN), flight data records.

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“…More recent studies have shown more potential in using soft-computing approaches in the identification of vortex encounters. In [6], the authors use fuzzy logic (FL) to model and identify vortex encounters. FL tolerates data imprecision and cope well with complexities in modeling the vortex encounters.…”

Section: Introductionmentioning

confidence: 99%

Airplane Vortex Encounters Identification Using Multilayer Feed-Forward Neural Networks

Bouslama¹,

Al-Mahadin²

2019

IJMLC

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The encounter of vortices generated by a leading aircraft during takeoff or landing can be a source of hazard to a following aircraft. In spite of airport efforts to keep safe separation distances between aircrafts, a number of them encounter severe vortices each year. It has been challenging to accurately identify those encounters by manual approaches. To mitigate the impact of vortex encounters on an aircraft, it is important that more reliable identification techniques be developed. This research is a contribution towards the automatic identification of vortex encounters using artificial neural networks. Multilayer feedforward neural networks are trained using the back-propagation learning algorithm to classify flight events into either vortex encounters or other events. Using salient inputs such as aircraft roll angle, normal acceleration and lateral acceleration, the neural networks are able to achieve an overall average identification rate of about 88%. These results confirm the authors' earlier assumption on using a reduced set of critical inputs to properly classify aircraft vortex encounters.

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Automatic identification of wake vortex traverse by transport aircraft using fuzzy logic

Cited by 5 publications

References 18 publications

Developed Criteria to Improve Pilot Reporting of Airplane Vortex Encounters

Developed Criteria to Improve Pilot Reporting of Airplane Vortex Encounters

Recognition of Airplane Wing-Tip Vortices Encounters Using Neural Networks

Airplane Vortex Encounters Identification Using Multilayer Feed-Forward Neural Networks

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