Special Issue "Machine Learning Applications in Aviation Safety"

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editors

Prof. Dr. Dimitri Mavris
Website
Guest Editor
S.P. Langley NIA Distinguished Regents Professor, Director of the Aerospace Systems Design Laboratory, School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150, USA
Interests: multi-disciplinary design optimization; multi-disciplinary analysis; probabilistic design; aircraft design; propulsion design; rotorcraft; systems engineering; systems of systems and technology assessments
Dr. Tejas Puranik
Website SciProfiles
Guest Editor
Research Engineer II, Aerospace Systems Design Laboratory, School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150, USA
Interests: air transportation systems; safety and risk; machine learning; sustainability; deep learning; aviation big data; smart manufacturing

Special Issue Information

Dear Colleagues,

The present Special Issue entitled “Machine Learning Applications in Aviation Safety” focuses on topics related to the application of machine learning, deep learning, and other emerging data-driven techniques in the context of enhancing safety in aviation and the air transportation system. Machine learning and deep learning techniques have revolutionized many domains of application such as image recognition, natural language processing, autonomous driving, etc. These techniques have proved increasingly useful in the analysis of big data obtained from aviation operations in recent years. Therefore, this Special Issue solicits novel applications of such techniques for the goal of improving the safety and reliability of aviation operations—both commercial and general aviation. The applications could be intended for in-flight or retrospective analysis and conducted at individual aircraft level, fleet level, or system level. Authors are invited to submit full research articles or review manuscripts addressing (but not limited to) the following topics:

  • Data processing frameworks for handling big data in aviation domain;
  • Data fusion framework for leveraging multiple sources of information;
  • Predictive models for risk likelihood using aviation data;
  • Precursor identification for safety incidents, events, accidents using text/data mining;
  • Anomaly detection in air traffic or operations using flight data;
  • Challenges and opportunities in the application of machine learning in aviation safety data.

Moreover, the focal topics listed above are not meant to exclude articles from additional related areas. We are looking forward to receiving your submissions and kindly invite you to address the Guest Editors in case of further questions.

Prof. Dr. Dimitri Mavris
Dr. Tejas Puranik
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • safety
  • risk
  • precursors
  • anomaly detection
  • machine learning
  • deep learning
  • big data
  • air transportation system

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Critical Parameter Identification for Safety Events in Commercial Aviation Using Machine Learning
Aerospace 2020, 7(6), 73; https://doi.org/10.3390/aerospace7060073 - 04 Jun 2020
Abstract
In recent years, there has been a rapid growth in the application of data science techniques that leverage aviation data collected from commercial airline operations to improve safety. This paper presents the application of machine learning to improve the understanding of risk factors [...] Read more.
In recent years, there has been a rapid growth in the application of data science techniques that leverage aviation data collected from commercial airline operations to improve safety. This paper presents the application of machine learning to improve the understanding of risk factors during flight and their causal chains. With increasing complexity and volume of operations, rapid accumulation and analysis of this safety-related data has the potential to maintain and even lower the low global accident rates in aviation. This paper presents the development of an analytical methodology called Safety Analysis of Flight Events (SAFE) that synthesizes data cleaning, correlation analysis, classification-based supervised learning, and data visualization schema to streamline the isolation of critical parameters and the elimination of tangential factors for safety events in aviation. The SAFE methodology outlines a robust and repeatable framework that is applicable across heterogeneous data sets containing multiple aircraft, airport of operations, and phases of flight. It is demonstrated on Flight Operations Quality Assurance (FOQA) data from a commercial airline through use cases related to three safety events, namely Tire Speed Event, Roll Event, and Landing Distance Event. The application of the SAFE methodology yields a ranked list of critical parameters in line with subject-matter expert conceptions of these events for all three use cases. The work concludes by raising important issues about the compatibility levels of machine learning and human conceptualization of incidents and their precursors, and provides initial guidance for their reconciliation. Full article
(This article belongs to the Special Issue Machine Learning Applications in Aviation Safety)
Show Figures

Figure 1

Open AccessArticle
Aircraft Mode S Transponder Fingerprinting for Intrusion Detection
Aerospace 2020, 7(3), 30; https://doi.org/10.3390/aerospace7030030 - 18 Mar 2020
Abstract
Nowadays, aircraft safety is based on different systems and four of them share the same data-link protocol: Secondary Surveillance Radar, Automatic Dependent Surveillance System, Traffic Collision Avoidance System, and Traffic Information System use the Mode S protocol to send and receive information. This [...] Read more.
Nowadays, aircraft safety is based on different systems and four of them share the same data-link protocol: Secondary Surveillance Radar, Automatic Dependent Surveillance System, Traffic Collision Avoidance System, and Traffic Information System use the Mode S protocol to send and receive information. This protocol does not provide any kind of authentication, making some of these applications vulnerable to cyberattacks. In this paper, an intrusion detection mechanism based on transmitter Radio Frequency (RF) fingerprinting is proposed to distinguish between legitimate messages and fake ones. The proposed transmitter signature is described and an intrusion detection algorithm is developed and evaluated in case of different intrusion configurations, also with the use of real recorded data. The results show that it is possible to detect the presence of fake messages with a high probability of detection and very low probability of false alarm. Full article
(This article belongs to the Special Issue Machine Learning Applications in Aviation Safety)
Show Figures

Figure 1

Back to TopTop