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Luis Basora
ONERA DTIS, Université de Toulouse, 31055 Toulouse, France

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Journal article
Published: 07 April 2021 in Aerospace
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Predictive maintenance has received considerable attention in the aviation industry where costs, system availability and reliability are major concerns. In spite of recent advances, effective health monitoring and prognostics for the scheduling of condition-based maintenance operations is still very challenging. The increasing availability of maintenance and operational data along with recent progress made in machine learning has boosted the development of data-driven prognostics and health management (PHM) models. In this paper, we describe the data workflow in place at an airline for the maintenance of an aircraft system and highlight the difficulties related to a proper labelling of the health status of such systems, resulting in a poor suitability of supervised learning techniques. We focus on investigating the feasibility and the potential of semi-supervised anomaly detection methods for the health monitoring of a real aircraft system. Proposed methods are evaluated on large volumes of real sensor data from a cooling unit system on a modern wide body aircraft from a major European airline. For the sake of confidentiality, data has been anonymized and only few technical and operational details about the system had been made available. We trained several deep neural network autoencoder architectures on nominal data and used the anomaly scores to calculate a health indicator. Results suggest that high anomaly scores are correlated with identified failures in the maintenance logs. Also, some situations see an increase in the anomaly score for several flights prior to the system’s failure, which paves a natural way for early fault identification.

ACS Style

Luis Basora; Paloma Bry; Xavier Olive; Floris Freeman. Aircraft Fleet Health Monitoring with Anomaly Detection Techniques. Aerospace 2021, 8, 103 .

AMA Style

Luis Basora, Paloma Bry, Xavier Olive, Floris Freeman. Aircraft Fleet Health Monitoring with Anomaly Detection Techniques. Aerospace. 2021; 8 (4):103.

Chicago/Turabian Style

Luis Basora; Paloma Bry; Xavier Olive; Floris Freeman. 2021. "Aircraft Fleet Health Monitoring with Anomaly Detection Techniques." Aerospace 8, no. 4: 103.

Proceedings
Published: 01 January 2020 in Proceedings
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The large amount of aircraft trajectory data publicly available through open data sources like the OpenSky Network presents a wide range of possibilities for monitoring and post-operational analysis of air traffic performance. This contribution addresses the automatic identification of operational events associated with trajectories. This is a challenging task that can be tackled with both empirical, rule-based methods and statistical, data-driven approaches. In this paper, we first propose a taxonomy of significant events, including usual operations such as take-off, Instrument Landing System (ILS) landing and holding, as well as less usual operations like firefighting, in-flight refuelling and navigational calibration. Then, we introduce different rule-based and statistical methods for detecting a selection of these events. The goal is to compare candidate methods and to determine which of the approaches performs better in each situation.

ACS Style

Xavier Olive; Junzi Sun; Adrien Lafage; Luis Basora. Detecting Events in Aircraft Trajectories: Rule-Based and Data-Driven Approaches. Proceedings 2020, 59, 8 .

AMA Style

Xavier Olive, Junzi Sun, Adrien Lafage, Luis Basora. Detecting Events in Aircraft Trajectories: Rule-Based and Data-Driven Approaches. Proceedings. 2020; 59 (1):8.

Chicago/Turabian Style

Xavier Olive; Junzi Sun; Adrien Lafage; Luis Basora. 2020. "Detecting Events in Aircraft Trajectories: Rule-Based and Data-Driven Approaches." Proceedings 59, no. 1: 8.

Review
Published: 30 October 2019 in Aerospace
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Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

ACS Style

Luis Basora; Xavier Olive; Thomas Dubot. Recent Advances in Anomaly Detection Methods Applied to Aviation. Aerospace 2019, 6, 117 .

AMA Style

Luis Basora, Xavier Olive, Thomas Dubot. Recent Advances in Anomaly Detection Methods Applied to Aviation. Aerospace. 2019; 6 (11):117.

Chicago/Turabian Style

Luis Basora; Xavier Olive; Thomas Dubot. 2019. "Recent Advances in Anomaly Detection Methods Applied to Aviation." Aerospace 6, no. 11: 117.

Preprint
Published: 29 September 2019
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Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. We cover especially unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

ACS Style

Luis Basora; Xavier Olive; Thomas Dubot. Recent Advances in Anomaly Detection Methods applied to Aviation. 2019, 1 .

AMA Style

Luis Basora, Xavier Olive, Thomas Dubot. Recent Advances in Anomaly Detection Methods applied to Aviation. . 2019; ():1.

Chicago/Turabian Style

Luis Basora; Xavier Olive; Thomas Dubot. 2019. "Recent Advances in Anomaly Detection Methods applied to Aviation." , no. : 1.