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Aerospace
Special Issues
Machine Learning Applications in Aviation Safety
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Machine Learning Applications in Aviation Safety

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Air Traffic and Transportation".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 36693

Special Issue Editors

Prof. Dr. Dimitri Mavris

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Guest Editor
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Interests: multi-disciplinary design optimization; multi-disciplinary analysis; probabilistic design; aircraft design; propulsion design; rotorcraft; systems engineering; systems of systems and technology assessments
Special Issues, Collections and Topics in MDPI journals
Dr. Tejas Puranik

E-Mail
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 submissions that pass pre-check are 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 2400 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 (5 papers)

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Research

21 pages, 4358 KiB  
Article
Classification and Analysis of Go-Arounds in Commercial Aviation Using ADS-B Data
by Satvik G. Kumar, Samantha J. Corrado, Tejas G. Puranik and Dimitri N. Mavris
Aerospace 2021, 8(10), 291; https://doi.org/10.3390/aerospace8100291 - 09 Oct 2021
Cited by 13 | Viewed by 3391
Abstract
Go-arounds are a necessary aspect of commercial aviation and are conducted after a landing attempt has been aborted. It is necessary to conduct go-arounds in the safest possible manner, as go-arounds are the most safety-critical of operations. Recently, the increased availability of data, [...] Read more.
Go-arounds are a necessary aspect of commercial aviation and are conducted after a landing attempt has been aborted. It is necessary to conduct go-arounds in the safest possible manner, as go-arounds are the most safety-critical of operations. Recently, the increased availability of data, such as ADS-B, has provided the opportunity to leverage machine learning and data analytics techniques to assess aviation safety events. This paper presents a framework to detect go-around flights, identify relevant features, and utilize unsupervised clustering algorithms to categorize go-around flights, with the objective of gaining insight into aspects of typical, nominal go-arounds and factors that contribute to potentially abnormal or anomalous go-arounds. Approaches into San Francisco International Airport in 2019 were examined. A total of 890 flights that conducted a single go-around were identified by assessing an aircraft’s vertical rate, altitude, and cumulative ground track distance states during approach. For each flight, 61 features relevant to go-around incidents were identified. The HDBSCAN clustering algorithm was leveraged to identify nominal go-arounds, anomalous go-arounds, and a third cluster of flights that conducted a go-around significantly later than other go-around trajectories. Results indicate that the go-arounds detected as being anomalous tended to have higher energy states and deviations from standard procedures when compared to the nominal go-arounds during the first approach, prior to the go-around. Further, an extensive comparison of energy states between nominal flights, anomalous flights, the first approach prior to the go-around, and the second approach following the go-around is presented. Full article
(This article belongs to the Special Issue Machine Learning Applications in Aviation Safety)
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22 pages, 3361 KiB  
Article
Natural Language Processing Based Method for Clustering and Analysis of Aviation Safety Narratives
by Rodrigo L. Rose, Tejas G. Puranik and Dimitri N. Mavris
Aerospace 2020, 7(10), 143; https://doi.org/10.3390/aerospace7100143 - 28 Sep 2020
Cited by 27 | Viewed by 6967
Abstract
The complexity of commercial aviation operations has grown substantially in recent years, together with a diversification of techniques for collecting and analyzing flight data. As a result, data-driven frameworks for enhancing flight safety have grown in popularity. Data-driven techniques offer efficient and repeatable [...] Read more.
The complexity of commercial aviation operations has grown substantially in recent years, together with a diversification of techniques for collecting and analyzing flight data. As a result, data-driven frameworks for enhancing flight safety have grown in popularity. Data-driven techniques offer efficient and repeatable exploration of patterns and anomalies in large datasets. Text-based flight safety data presents a unique challenge in its subjectivity, and relies on natural language processing tools to extract underlying trends from narratives. In this paper, a methodology is presented for the analysis of aviation safety narratives based on text-based accounts of in-flight events and categorical metadata parameters which accompany them. An extensive pre-processing routine is presented, including a comparison between numeric models of textual representation for the purposes of document classification. A framework for categorizing and visualizing narratives is presented through a combination of k-means clustering and 2-D mapping with t-Distributed Stochastic Neighbor Embedding (t-SNE). A cluster post-processing routine is developed for identifying driving factors in each cluster and building a hierarchical structure of cluster and sub-cluster labels. The Aviation Safety Reporting System (ASRS), which includes over a million de-identified voluntarily submitted reports describing aviation safety incidents for commercial flights, is analyzed as a case study for the methodology. The method results in the identification of 10 major clusters and a total of 31 sub-clusters. The identified groupings are post-processed through metadata-based statistical analysis of the learned clusters. The developed method shows promise in uncovering trends from clusters that are not evident in existing anomaly labels in the data and offers a new tool for obtaining insights from text-based safety data that complement existing approaches. Full article
(This article belongs to the Special Issue Machine Learning Applications in Aviation Safety)
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19 pages, 925 KiB  
Article
Unsupervised Anomaly Detection in Flight Data Using Convolutional Variational Auto-Encoder
by Milad Memarzadeh, Bryan Matthews and Ilya Avrekh
Aerospace 2020, 7(8), 115; https://doi.org/10.3390/aerospace7080115 - 08 Aug 2020
Cited by 59 | Viewed by 9755
Abstract
The modern National Airspace System (NAS) is an extremely safe system and the aviation industry has experienced a steady decrease in fatalities over the years. This is in part due the airlines, manufacturers, FAA, and research institutions all continually working to improve the [...] Read more.
The modern National Airspace System (NAS) is an extremely safe system and the aviation industry has experienced a steady decrease in fatalities over the years. This is in part due the airlines, manufacturers, FAA, and research institutions all continually working to improve the safety of the operations. However, the current approach for identifying vulnerabilities in NAS operations leverages domain expertise using knowledge about how the system should behave within the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to assess risk based on pre-defined safety vulnerabilities. Moreover, state-of-the-art machine learning models that are developed for event detection in aerospace data usually rely on supervised learning. However, in many real-world problems, such as flight safety, creating labels for the data requires specialized expertise that is time consuming and therefore largely impractical. To address this challenge, we develop a Convolutional Variational Auto-Encoder (CVAE), an unsupervised deep generative model for anomaly detection in high-dimensional time-series data. Validating on Yahoo’s benchmark data as well as a case study of identifying anomalies in commercial flights’ take-offs, we show that CVAE outperforms both classic and deep learning-based approaches in precision and recall of detecting anomalies. Full article
(This article belongs to the Special Issue Machine Learning Applications in Aviation Safety)
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24 pages, 1002 KiB  
Article
Critical Parameter Identification for Safety Events in Commercial Aviation Using Machine Learning
by HyunKi Lee, Sasha Madar, Santusht Sairam, Tejas G. Puranik, Alexia P. Payan, Michelle Kirby, Olivia J. Pinon and Dimitri N. Mavris
Aerospace 2020, 7(6), 73; https://doi.org/10.3390/aerospace7060073 - 04 Jun 2020
Cited by 38 | Viewed by 6449
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)
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16 pages, 1519 KiB  
Article
Aircraft Mode S Transponder Fingerprinting for Intrusion Detection
by Mauro Leonardi and Fabrizio Gerardi
Aerospace 2020, 7(3), 30; https://doi.org/10.3390/aerospace7030030 - 18 Mar 2020
Cited by 13 | Viewed by 6654
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)
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