Anomaly Detection Using Temporal Logic Based Learning for Terminal Airspace Operations

Deshmukh, Raj; Hwang, Inseok

doi:10.2514/6.2019-0682

Cited by 10 publications

(17 citation statements)

References 5 publications

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“…The application area related to the identification of significant events in air traffic operations is particularly rich in terms of the number and variety of the anomaly methods applied. While traditional techniques are widely used, there exists also some attempts to apply recent advances in temporal-logic learning [103,111], RNN [58] and advanced autoencoders (e.g., ConvLSTM-AE [86]). The vast majority of the research in this application area concern the detection of anomalies relevant to safety, although we provided also a few examples of anomalies related to potential cyberattacks or air traffic congestion.…”

Section: Discussionmentioning

confidence: 99%

“…The approach has been applied to several domains including naval surveillance and train braking system [73]. Concerning the aviation domain, Deshmukh et al [103] has recently used the approach to detect anomalies in the terminal airspace operations (more details in Section 4.1).…”

Section: Recent Advances In Temporal Logic-based Learningmentioning

confidence: 99%

“…Deshmukh et al [103] propose a temporal logic based anomaly detection algorithm (TempAD) applicable to trajectories in the terminal airspace. The algorithm, based on a temporal-logic learning approach [71][72][73], can learn human-readable mathematical expressions from data which facilitates the feedback and interaction with operational experts.…”

Section: Temporal-logic Learning Based Approachesmentioning

confidence: 99%

“…Following up this research, Deshmukh et al [111] develop a supervised precursor detection algorithm called reactive TempAD by correlating surveillance data to specific anomalies identified by the TempAD algorithm [103]. Thus, the prediction of an anomaly is performed by identifying events that precede the occurrence of an anomaly, which are called precursors.…”

Section: Temporal-logic Learning Based Approachesmentioning

confidence: 99%

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Recent Advances in Anomaly Detection Methods Applied to Aviation

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 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.

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Section: Discussionmentioning

confidence: 99%

Section: Recent Advances In Temporal Logic-based Learningmentioning

confidence: 99%

Section: Temporal-logic Learning Based Approachesmentioning

confidence: 99%

Section: Temporal-logic Learning Based Approachesmentioning

confidence: 99%

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Recent Advances in Anomaly Detection Methods Applied to Aviation

2019

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“…Nanduri and Sherry [58] present a regression-based approach applied to simulated FOQA-like data [117] Following up this research, Deshmukh et al [111] develop a supervised precursor detection algorithm called reactive TempAD by correlating surveillance data to specific anomalies identified by the TempAD algorithm [103]. Thus, the prediction of an anomaly is performed by identifying events that precede the occurrence of an anomaly, which are called precursors.…”

Section: Reconstruction-based Approachesmentioning

confidence: 99%

Recent Advances in Anomaly Detection Methods applied to Aviation

Basora¹,

Olivé²,

Dubot³

2019

Preprint

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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.

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Detection and identification of significant events in historical aircraft trajectory data

Olivé

Basora

2020

Transportation Research Part C: Emerging Technologies

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A large amount of data is produced every day by stakeholders of the Air Traffic Management (ATM) system, in particular airline operators, airports, and air navigation service provider (ANSP). Most data is kept private for many reasons, including commercial and security concerns. More than data, shared information is precious, as it leverages intelligent decision-making support tools designed to smooth daily operations.We present a framework to detect, identify and characterise anomalies in past aircraft trajectory data. It is based on an open source of ADS-B based aircraft trajectories, and extracted information can benefit a wide range of stakeholders: Air Traffic Control (ATC) training centres could play more realistic simulations; ANSP may improve capacity indicators; academics improve safety models and risk estimations; and commercial stakeholders, like airlines and airports, may use such information to improve short-term predictions and optimise their operations.The technique is based on autoencoding artificial neural networks applied on flows of trajectories, which provide a useful reading grid associating cluster analysis with quantified level of abnormality. In particular, we find that the highest anomaly scores correspond to poor weather conditions, whereas anomalies with a lower score relate to ATC tactical actions.

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Anomaly Detection Using Temporal Logic Based Learning for Terminal Airspace Operations

Cited by 10 publications

References 5 publications

Recent Advances in Anomaly Detection Methods Applied to Aviation

Recent Advances in Anomaly Detection Methods Applied to Aviation

Recent Advances in Anomaly Detection Methods applied to Aviation

Detection and identification of significant events in historical aircraft trajectory data

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