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Machine Learning for Aeronautics (2nd Edition)

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A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 14087

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Special Issue Editors

Dr. Olivia J. Pinon Fischer

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Guest Editor

Aerospace Systems Design Laboratory, School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Interests: digital engineering; digital twin/thread; ML/AI in engineering design; aerospace and defense
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Dimitri Mavris

E-Mail Website
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

Special Issue Information

Dear Colleagues,

From enhancing aircraft design and manufacturing to enabling virtual testing, accelerating the certification of novel concepts, optimizing flight and maintenance operations, revolutionizing air traffic management, and improving aviation safety, the integration of machine learning offers unparalleled opportunities for innovation.

This Special Issue aims to showcase the latest research, case studies, and innovative ML techniques that are pushing the boundaries of what is possible in aeronautics. We invite contributions from researchers, engineers, and practitioners who are working at the intersection of machine learning and aerospace technology. Whether through the development of advanced algorithms for flight control, the application of predictive maintenance for aircraft systems, or the use of machine learning to improve aerodynamic designs, your work is contributing to the smarter, safer, and more efficient operation of aircraft and air transport systems.

In this Special Issue, we look forward to sharing insights that will not only advance the state of the art in aeronautical engineering but also inspire further innovation in the application of machine learning within the industry. In particular, authors are invited to submit full research articles or review manuscripts that address (but are not limited to) the following topics:

Application of AI/ML to requirement engineering;
Application of AI/ML to design (e.g., generative design);
Application of AI/ML to aircraft development, modeling, and testing;
Application of AI/ML to manufacturing and factory automation;
Application of AI/ML in support of certification by analysis;
Optimization of flight profile/performance;
Real-time fault detection and predictive maintenance;
Application of AI/ML to pilot training;
Application of AI/ML to aviation safety;
Application of AI/ML to air traffic management;
Application of AI to environmental impact assessment in aviation;
Application of AI/ML to autonomous flight;
Application of AI/ML to flight control and navigation;
Cybersecurity in aviation systems;
Additional related areas.

We look forward to receiving your submissions. Please contact the Guest Editor for any further questions.

Dr. Olivia J. Pinon Fischer
Prof. Dr. Dimitri Mavris
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

application of AI/ML to requirement engineering
application of AI/ML to design (e.g., generative design)
application of AI/ML to aircraft development, modeling, and testing
application of AI/ML to manufacturing and factory automation
application of AI/ML in support of certification by analysis
optimization of flight profile/performance
real-time fault detection and predictive maintenance
application of AI/ML to pilot training
application of AI/ML to aviation safety
application of AI/ML to air traffic management
application of AI to environmental impact assessment in aviation
application of AI/ML to autonomous flight
application of AI/ML to flight control and navigation
cybersecurity in aviation systems
additional related areas

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Related Special Issue

Machine Learning for Aeronautics in Aerospace (19 articles)

Published Papers (11 papers)

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Research

21 pages, 6370 KiB

Open AccessArticle

Aero-Engine Remain Useful Life Estimation via Scope-Coordinated Attention-Based Network

by Zheng Liao, Sijie Liu, Jin Li, Shuai Ma and Gang Li

Aerospace 2025, 12(3), 259; https://doi.org/10.3390/aerospace12030259 - 19 Mar 2025

Viewed by 306

Abstract

Research on the assessment of the remaining useful life (RUL) has garnered significant attention because of its critical relevance in prognostics and health management (PHM) across various sectors. Recently, data-driven methodologies have become increasingly important for RUL prediction. However, these methods often struggle to capture long-term dependencies and possess a limited receptive field, restricting their effectiveness in various RUL prediction scenarios. To address these limitations, this study proposes a novel approach called the scope-coordinated attention-based (SCAB) network for RUL prediction. The initial design features a novel multichannel feature integration block, which aims to effectively capture and integrate essential information from raw sensor data. Additionally, it is designed to expand the receptive field by capturing rich and diverse features for improved representation. Subsequently, a dual-attention block refines information and further expands the receptive field in both the channel and spatial domain. Moreover, a feature pyramid block with adaptive self-attention is developed to effectively capture long-term dependencies, further enhancing the information’s detail and features by the multiscale feature fusion mechanism. The efficacy of the proposed SCAB model for RUL estimation was validated using the C-MAPSS public dataset. In comparison experiments, the SCAB model outperformed other methods in the FD002 subset while demonstrating excellent performances in FD001, FD003, and FD004. These results confirm that the SCAB model exhibits robust and superior performance in RUL prediction across various aeroengine scenarios. Full article

(This article belongs to the Special Issue Machine Learning for Aeronautics (2nd Edition))

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21 pages, 2447 KiB

Open AccessArticle

Explainable Supervised Learning Models for Aviation Predictions in Australia

by Aziida Nanyonga, Hassan Wasswa, Keith Joiner, Ugur Turhan and Graham Wild

Aerospace 2025, 12(3), 223; https://doi.org/10.3390/aerospace12030223 - 9 Mar 2025

Viewed by 711

Abstract

Artificial intelligence (AI) has demonstrated success across various industries; however, its adoption in aviation remains limited due to concerns regarding the interpretability of AI models, which often function as black box systems with opaque decision-making processes. Given the safety-critical nature of aviation, the lack of transparency in AI-generated predictions poses significant challenges for industry stakeholders. This study investigates the classification performance of multiple supervised machine learning models and employs SHapley Additive exPlanations (SHAPs) to provide global model explanations, identifying key features that influence decision boundaries. To address the issue of class imbalance in the Australian Transport Safety Bureau (ATSB) dataset, a Variational Autoencoder (VAE) is also employed for data augmentation. A comparative evaluation of four machine learning algorithms is conducted for a three-class classification task:—Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and a deep neural network (DNN) comprising five hidden layers. The results demonstrate a competitive performance across accuracy, precision, recall, and F1-score metrics, highlighting the effectiveness of explainable AI techniques in enhancing model transparency and fostering trust in AI-driven aviation safety applications. Full article

(This article belongs to the Special Issue Machine Learning for Aeronautics (2nd Edition))

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17 pages, 2879 KiB

Open AccessArticle

Aviation Safety at the Brink: Unveiling the Hidden Dangers of Wind-Shear-Related Aircraft-Missed Approaches

by Afaq Khattak, Jianping Zhang, Pak-Wai Chan, Feng Chen and Abdulrazak H. Almaliki

Aerospace 2025, 12(2), 126; https://doi.org/10.3390/aerospace12020126 - 7 Feb 2025

Viewed by 739

Abstract

Aircraft-missed approaches pose significant safety challenges, particularly under adverse weather conditions like wind shear. This study examines the critical factors influencing wind-shear-related missed approaches at Hong Kong International Airport (HKIA) using Pilot Report (PIREP) data from 2015 to 2023. A Binary Logistic Model (BLM) with L1 (Lasso) and L2 (Ridge) regularization was applied to both balanced and imbalanced datasets, with the balanced dataset created using the Synthetic Minority Oversampling Technique (SMOTE). The performance of the BLM on the balanced data demonstrated a good model fit, with Hosmer–Lemeshow statistics of 5.91 (L1) and 5.90 (L2). The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were slightly lower for L1 regularization, at 1528.77 and 1574.35, respectively, compared to 1528.86 and 1574.66 for L2. Cohen’s Kappa values were 0.266 for L1 and 0.253 for L2, reflecting moderate agreement between observed and predicted outcomes and improved performance compared to the imbalanced data. The analysis identified designated-approach runway, aircraft classification, wind shear source, and vertical proximity of wind shear to runway as the most influential factors. Runways 07R and 07C, gust fronts as wind shear sources, and wind shear occurring within 400 ft of the runway posed the highest risk for missed approaches. Narrow-body aircrafts also demonstrated greater susceptibility to turbulence-induced missed approaches. These findings show the importance of addressing these risk factors and enhancing safety protocols for adverse weather conditions. Full article

(This article belongs to the Special Issue Machine Learning for Aeronautics (2nd Edition))

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20 pages, 2163 KiB

Open AccessArticle

Identifying Human Factors in Aviation Accidents with Natural Language Processing and Machine Learning Models

by Flávio L. Lázaro, Tomás Madeira, Rui Melicio, Duarte Valério and Luís F. F. M. Santos

Aerospace 2025, 12(2), 106; https://doi.org/10.3390/aerospace12020106 - 31 Jan 2025

Cited by 1 | Viewed by 1440

Abstract

The use of machine learning techniques to identify contributing factors in air incidents has grown significantly, helping to identify and prevent accidents and improve air safety. In this paper, classifier models such as LS, KNN, Random Forest, Extra Trees, and XGBoost, which have proven effective in classification tasks, are used to analyze incident reports parsed with natural language processing (NLP) techniques, to uncover hidden patterns and prevent future incidents. Metrics such as precision, recall, F1-score and accuracy are used to assess the degree of correctness of the predictive models. The adjustment of hyperparameters is obtained with Grid Search and Bayesian Optimization. KNN had the best predictive rating, followed by Random Forest and Extra Trees. The results indicate that the use of machine learning tools to classify incidents and accidents helps to identify their root cause, improving situational decision-making. Full article

(This article belongs to the Special Issue Machine Learning for Aeronautics (2nd Edition))

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30 pages, 13525 KiB

Open AccessArticle

An Innovative Aircraft Skin Damage Assessment Using You Only Look Once-Version9: A Real-Time Material Evaluation System for Remote Inspection

by Kuo-Chien Liao, Jirayu Lau and Muhamad Hidayat

Aerospace 2025, 12(1), 31; https://doi.org/10.3390/aerospace12010031 - 6 Jan 2025

Cited by 1 | Viewed by 1100

Abstract

Aircraft safety is the aviation industry’s primary concern. Inspections must be conducted before each flight to ensure the integrity of the aircraft. To meet the increasing demand for engineers, a system capable of detecting surface defects on aircraft was designed to reduce the workload of the inspection process. The system utilizes the real-time object detection capabilities of the you only look once-version 9 (YOLO v9) algorithm, combined with imagery captured from an unmanned aerial vehicle (UAV)-based aerial platform. This results in a system capable of detecting defects such as cracks and dents on the aircraft’s surface, even in areas that are difficult to reach, such as the upper surfaces of the wings or the higher parts of the fuselage. With the introduction of a Real-Time Messaging Protocol (RTMP) server, the results can be monitored via artificial intelligence (AI) and Internet of Things (IoT) devices in real time for further evaluation. The experimental results confirmed an effective recognition of defects, with a mean average precision (mAP@0.5) of 0.842 for all classes, the highest score being 0.938 for dents and the lowest value 0.733 for the paint-off class. This study demonstrates the potential for developing image detection technology with AI for the aviation industry. Full article

(This article belongs to the Special Issue Machine Learning for Aeronautics (2nd Edition))

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28 pages, 17406 KiB

Open AccessArticle

Enhancing Multi-Hole Pressure Probe Data Processing in Turbine Cascade Experiments Using Structural Risk Minimization Principle

by Ming Ni, Zuojun Wei, Weimin Deng, Haibo Tao, Guangming Ren and Xiaohua Gan

Aerospace 2024, 11(12), 973; https://doi.org/10.3390/aerospace11120973 - 26 Nov 2024

Viewed by 872

Abstract

Multi-hole pressure probes are crucial for turbomachinery flow measurements, yet conventional data processing methods often lack generalization for complex flows. This study introduces an innovative approach by integrating machine learning techniques with the structural risk minimization (SRM) principle, significantly enhancing the generalization capability of regression models. A comprehensive framework has been developed, combining SRM-based machine learning regression methods, such as ridge regression and kernel ridge regression, with hyperparameter optimization and S-fold cross-validation, to ensure robust model selection and accuracy. Validated using the McCormick function and applied to VKI-RG transonic turbine cascade measurements, the SRM-based methods demonstrated superior performance over traditional empirical risk minimization approaches, with lower error ratios and higher

R^{2}

values. Novel insights from SHAP analysis revealed subtle but significant differences in aerodynamic parameters, including a

0.63122 °

discrepancy in exit flow angle predictions, guiding the probe design and calibration strategies. This study presents a holistic workflow for improving multi-hole pressure probe measurements under high-subsonic conditions, representing a meaningful enhancement over traditional empirical methods and providing valuable references for practical applications. Full article

(This article belongs to the Special Issue Machine Learning for Aeronautics (2nd Edition))

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22 pages, 4437 KiB

Open AccessArticle

Model-Assisted Probabilistic Neural Networks for Effective Turbofan Fault Diagnosis

by Christoforos Romesis, Nikolaos Aretakis and Konstantinos Mathioudakis

Aerospace 2024, 11(11), 913; https://doi.org/10.3390/aerospace11110913 - 6 Nov 2024

Viewed by 1355

Abstract

A diagnostic method for gas-path faults of turbofan engines, relying on a Probabilistic Neural Network (PNN) coupled with a thermodynamic model of the engine, is presented. The novel aspect of the method is that its training information is generated dynamically by an accompanying Engine Performance Model. In the proposed approach, the PNN efficiently addresses the first step of a diagnostic process (i.e., detection of the faulty component at the current operating point), while with the aid of an adaptive engine model, the fault is then further isolated and identified. A description of the proposed method and training aspects of the PNN are presented. The method is applied to the case of a mixed-flow turbofan engine to diagnose common gas-path faults in compressors and turbines (i.e., fouling, FOD, erosion, and tip clearance). Its performance is evaluated using realistic fault data that may be acquired at various operating conditions within a flight envelope. Full article

(This article belongs to the Special Issue Machine Learning for Aeronautics (2nd Edition))

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22 pages, 1194 KiB

Open AccessArticle

Aviation-BERT-NER: Named Entity Recognition for Aviation Safety Reports

by Chetan Chandra, Yuga Ojima, Mayank V. Bendarkar and Dimitri N. Mavris

Aerospace 2024, 11(11), 890; https://doi.org/10.3390/aerospace11110890 - 29 Oct 2024

Viewed by 2017

Abstract

This work introduces Aviation-BERT-NER, a Named Entity Recognition (NER) system tailored for aviation safety reports, building on the Aviation-BERT base model developed at the Georgia Institute of Technology’s Aerospace Systems Design Laboratory. This system integrates aviation domain-specific data, including aircraft types, manufacturers, quantities, and aviation terminology, to identify named entities critical for aviation safety analysis. A key innovation of Aviation-BERT-NER is its template-based approach to fine-tuning, which utilizes structured datasets to generate synthetic training data that mirror the complexity of real-world aviation safety reports. This method significantly improves the model’s generalizability and adaptability, enabling rapid updates and customization to meet evolving domain-specific requirements. The development process involved careful data preparation, including the synthesis of entity types and the generation of labeled datasets through template filling. Testing on real-world narratives from the National Transportation Safety Board (NTSB) database highlighted Aviation-BERT-NER’s robustness, with a precision of 95.34%, recall of 94.62%, and F1 score of 94.78% when evaluated over 50 manually annotated (BIO tagged) paragraphs. This work addresses a critical gap in English language NER models for aviation safety, promising substantial improvements in the analysis and understanding of aviation safety reports. Full article

(This article belongs to the Special Issue Machine Learning for Aeronautics (2nd Edition))

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24 pages, 5059 KiB

Open AccessArticle

Hazard Analysis for Massive Civil Aviation Safety Oversight Reports Using Text Classification and Topic Modeling

by Yaxi Xu, Zurui Gan, Rengang Guo, Xin Wang, Ke Shi and Pengfei Ma

Aerospace 2024, 11(10), 837; https://doi.org/10.3390/aerospace11100837 - 11 Oct 2024

Cited by 2 | Viewed by 1041

Abstract

There are massive amounts of civil aviation safety oversight reports collected each year in the civil aviation of China. The narrative texts of these reports are typically short texts, recording the abnormal events detected during the safety oversight process. In the construction of an intelligent civil aviation safety oversight system, the automatic classification of safety oversight texts is a key and fundamental task. However, all safety oversight reports are currently analyzed and classified into categories by manual work, which is time consuming and labor intensive. In recent years, pre-trained language models have been applied to various text mining tasks and have proven to be effective. The aim of this paper is to apply text classification to the mining of these narrative texts and to show that text classification technology can be a critical element of the aviation safety oversight report analysis. In this paper, we propose a novel method for the classification of narrative texts in safety oversight reports. Through extensive experiments, we validated the effectiveness of all the proposed components. The experimental results demonstrate that our method outperforms existing methods on the self-built civil aviation safety oversight dataset. This study undertakes a thorough examination of the precision and associated outcomes of the dataset, thereby establishing a solid basis for furnishing valuable insights to enhance data quality and optimize information. Full article

(This article belongs to the Special Issue Machine Learning for Aeronautics (2nd Edition))

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22 pages, 3818 KiB

Open AccessArticle

A Peak-Finding Siamese Convolutional Neural Network (PF-SCNN) for Aero-Engine Hot Jet FT-IR Spectrum Classification

by Shuhan Du, Wei Han, Zhenping Kang, Fengkun Luo, Yurong Liao and Zhaoming Li

Aerospace 2024, 11(9), 703; https://doi.org/10.3390/aerospace11090703 - 28 Aug 2024

Cited by 1 | Viewed by 984

Abstract

Aiming at solving difficulties related to aero-engine classification and identification, two telemetry Fourier transform infrared spectrometers are utilized to measure the infrared spectra of six types of aero-engine hot jets, and create a spectral data set, which is divided into a training set (80%), a validation set (10%), and a prediction set (10%). A peak-finding Siamese convolutional neural network (PF-SCNN) is used to match and classify the spectral data. During the training stage, the Siamese convolutional neural network (SCNN) is designed to extract spectral features and calculate the distance similarity. In order to improve the efficiency of the SCNN, a peak-finding method is introduced to extract the spectral peaks, which are used to train the model instead of the original spectral data. During the prediction stage, the trained model is used to calculate the similarity between the prediction set and the combined set of the training set and validation set, and the label of the most similar training data in each prediction set is used as the prediction label. The performance measures of the classification results include accuracy, precision, recall, confusion matrix, and F1-score. The experimental results show that the PF-SCNN can achieve a high classification accuracy rate of 99% and can complete the task of classifying the infrared spectra of aero-engine hot jets. Full article

(This article belongs to the Special Issue Machine Learning for Aeronautics (2nd Edition))

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19 pages, 12017 KiB

Open AccessArticle

AI-Based Anomaly Detection Techniques for Structural Fault Diagnosis Using Low-Sampling-Rate Vibration Data

by Yub Jung, Eun-Gyo Park, Seon-Ho Jeong and Jeong-Ho Kim

Aerospace 2024, 11(7), 509; https://doi.org/10.3390/aerospace11070509 - 24 Jun 2024

Cited by 5 | Viewed by 2236

Abstract

Rotorcrafts experience severe vibrations during operation. To ensure the safety of rotorcrafts, it is necessary to perform anomaly detection to detect small-scale structural faults in major components. To accurately detect small-scale faults before they grow to a fatal size, HR (high sampling rate) vibration data are required. However, to increase the efficiency of data storage media, only LR (low sampling rate) vibration data are generally collected during actual flight operation. Anomaly detection using only LR data can detect faults above a certain size, but may fail to detect small-scale faults. To address this problem, we propose an anomaly detection technique using the SR3 (Super-Resolution via Repeated Refinement) algorithm to upscale LR data to HR data, and then applying the LSTM-AE model. This technique is validated for two datasets (drone arm data, CWRU bearing data). First, the necessity for HR data is illustrated by showing that anomaly detection using LR data fails, and the upscaling performance of the SR3 algorithm is validated in the frequency and time domain. Finally, the anomaly detection for a structural fault diagnosis is performed for the upscaled data and the HR data using the LSTM-AE model. The quantitative evaluation of the Min–Max normalized reconstruction error distribution is performed through the MSE (Mean Square Error) value of the anomaly detection results. As a result, it is confirmed that the anomaly detection using upscaled test data can be performed as successfully as the anomaly detection using HR test data for both datasets by the proposed technique. Full article

(This article belongs to the Special Issue Machine Learning for Aeronautics (2nd Edition))

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