Energy-Based Metrics for Safety Analysis of General Aviation Operations

Puranik, Tejas G.; Jiménez, Hernando; Mavris, Dimitri N.

doi:10.2514/1.c034196

Cited by 36 publications

(31 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the aim of improving the safety of General Aviation operations, Puranik et al [105] propose a framework to identify anomalies based on a OC-SVM model. After a classical preprocessing phase to clean the raw multivariate time series data, a set of feature vectors corresponding to the energy metrics detailed in [112] are computed, such as the Specific Total Energy (STE) or the Specific Potential Energy (SPE). The DBSCAN algorithm is first applied to the feature vector in order to determine the number of clusters.…”

Section: Domain-based Approachesmentioning

confidence: 99%

Recent Advances in Anomaly Detection Methods Applied to Aviation

2019

View full text Add to dashboard Cite

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.

show abstract

Section: Domain-based Approachesmentioning

confidence: 99%

Recent Advances in Anomaly Detection Methods Applied to Aviation

2019

View full text Add to dashboard Cite

show abstract

“…Following reparameterization, the energy metrics at each point are evaluated. The complete list of energy metrics is provided in the appendix (Table 2) and a more detailed description of these can be found in Puranik et al [11]. As required for the metrics, the values of recorded parameters along with aircraft performance models and reference profiles are used.…”

Section: Feature Generationmentioning

confidence: 99%

“…These models are used for evaluating a number of energy based metrics which provide a measurable quantification of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions across a large fleet of aircraft with minimal amount of recorded parameters. In the previous work (Puranik et al [10,11]), the authors conducted a thorough literature review of existing energy based metrics used in aircraft performance studies and defined some completely new metrics using GA flight data. Methods of generating a data-driven reference profile of different metrics was demonstrated in Puranik et al [12].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in General-Aviation Operations Using Energy Metrics and Flight-Data Records

Puranik

Mavris

2018

Journal of Aerospace Information Systems

View full text Add to dashboard Cite

Among operations in the General Aviation community, one of the most important objectives is to improve safety across all flight regimes. Flight data monitoring or Flight Operations Quality Assurance programs have percolated in the General Aviation sector with the aim of improving safety by analyzing and evaluating flight data. Energy-based metrics provide measurable indications of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions. The use of data mining techniques for safety analysis, incident examination, and fault detection is gaining traction in the aviation community. In this paper, a generic methodology is presented for identifying anomalous flight data records from General Aviation operations in the approach and landing phase. Energybased metrics, identified in previous work, are used to generate feature vectors for each flight data record. Density-based clustering and one-class classification are then used together for anomaly detection using energy-based metrics. A demonstration of this methodology on a set of actual flight data records from routine operations as well as simulated flight data is presented highlighting its potential for retrospective safety analysis. Anomaly detection using energy metrics, specifically, is a novel application presented here.

show abstract

“…To address this issue, there are two main research directions to help identify and avoid unstable approaches. The first research direction is to focus on the energy management cited as a main factor of unstable approach, and to investigate the relationship between the energy history and the flight stability [6][7][8]. This research method can identify unstable approaches due to faulty energy management only.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection and Cause Analysis During Landing Approach Using Recurrent Neural Network

Mori

2021

Journal of Aerospace Information Systems

View full text Add to dashboard Cite

The stabilized approach is important to avoid aircraft accidents during landing. Although there are many possible factors that can lead to an unstable approach, actions can be taken to mitigate the risk if the reason for unstable approaches is identified properly. However, because unstable approaches are rarely observed, it is difficult to identify the relevant reasons by analyzing unstable approaches only. Instead, the author proposes to identify untypical flights, which are not necessarily categorized into unstable approaches, by using neural network. Using the proposed method, more flights are available to identify the reason of unstable approaches. The stability index is estimated by neural network considering the current flight conditions such as wind and initial deviation. Therefore, untypical flights can be detected by comparing the actual stability index and the estimated stability index. The long-term interaction between the stability index and flight conditions is modeled by using gated recurrent unit. As a result of modeling, some of flights are categorized into untypical flights, and the false localizer beam is observed for most of these flights, which could be a potential hazard for unstable approaches. The proposed approach has a big potential to identify such potential hazards based on historical flight data.

show abstract

Energy-Based Metrics for Safety Analysis of General Aviation Operations

Cited by 36 publications

References 26 publications

Recent Advances in Anomaly Detection Methods Applied to Aviation

Recent Advances in Anomaly Detection Methods Applied to Aviation

Anomaly Detection in General-Aviation Operations Using Energy Metrics and Flight-Data Records

Anomaly Detection and Cause Analysis During Landing Approach Using Recurrent Neural Network

Contact Info

Product

Resources

About