Comparison of fault detection and isolation methods for a small unmanned aircraft

Venkataraman, Raghu; Bauer, Péter; Seiler, Peter; Vanek, Bálint

doi:10.1016/j.conengprac.2018.12.002

Cited by 30 publications

(11 citation statements)

References 45 publications

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“…The figures also show how the variance of the prediction error stabilizes after the model stabilization. While this work and [27] provide a dataset suitable for benchmarking different methods, to the best of our knowledge, there has been no benchmark dataset available prior to this work to enable direct comparison with the published results of similar works like Venkataraman et al [28] and Bauer et al [29]. The use of our dataset in the future will enable the comparison of methods to the state-of-the-art.…”

Section: Resultsmentioning

confidence: 99%

Automatic Real-time Anomaly Detection for Autonomous Aerial Vehicles

Keipour

Mousaei

Scherer

2019

2019 International Conference on Robotics and Automation (ICRA)

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The recent increase in the use of aerial vehicles raises concerns about the safety and reliability of autonomous operations. There is a growing need for methods to monitor the status of these aircraft and report any faults and anomalies to the safety pilot or to the autopilot to deal with the emergency situation. In this paper, we present a real-time approach using the Recursive Least Squares method to detect anomalies in the behavior of an aircraft. The method models the relationship between correlated input-output pairs online and uses the model to detect the anomalies. The result is an easy-to-deploy anomaly detection method that does not assume a specific aircraft model and can detect many types of faults and anomalies in a wide range of autonomous aircraft. The experiments on this method show a precision of 88.23%, recall of 88.23% and 86.36% accuracy for over 22 flight tests. The other contribution is providing a new fault detection open dataset for autonomous aircraft, which contains complete data and the ground truth for 22 fixed-wing flights with eight different types of mid-flight actuator failures to help future fault detection research for aircraft.

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Section: Resultsmentioning

confidence: 99%

Automatic Real-time Anomaly Detection for Autonomous Aerial Vehicles

Keipour

Mousaei

Scherer

2019

2019 International Conference on Robotics and Automation (ICRA)

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“…The results reported by these methods may be very different from the real data, making a comparison between these methods with the other methods tested on real flight tests difficult. Even many of the methods tested on the real flight data only report a minimal number of tests (Bu et al, 2017; Lin et al, 2010; Sun et al, 2017) and only a few proposed methods have completed a reasonable number of tests on the real flight data (Keipour et al, 2019; Venkataraman et al, 2019). Providing a large dataset to the FDI and AD community working on unmanned aerial vehicles (UAVs) will open the opportunity to test the proposed methods on real data and to compare the results with other methods.…”

Section: Introductionmentioning

confidence: 99%

ALFA: A dataset for UAV fault and anomaly detection

Keipour

Mousaei

Scherer

2020

The International Journal of Robotics Research

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We present a dataset of several fault types in control surfaces of a fixed-wing unmanned aerial vehicle (UAV) for use in fault detection and isolation (FDI) and anomaly detection (AD) research. Currently, the dataset includes processed data for 47 autonomous flights with 23 sudden full engine failure scenarios and 24 scenarios for 7 other types of sudden control surface (actuator) faults, with a total of 66 minutes of flight under normal conditions and 13 minutes of post-fault flight time. It additionally includes many hours of raw data of fully autonomous, autopilot-assisted and manual flights with tens of fault scenarios. The ground truth of the time and type of faults is provided in each scenario to enable evaluation of the methods using the dataset. We have also provided the helper tools in several programming languages to load and work with the data and to help the evaluation of a detection method using the dataset. A set of metrics is proposed to help to compare different methods using the dataset. Most of the current fault detection methods are evaluated in simulation and, as far as we know, this dataset is the only one providing the real flight data with faults in such capacity. We hope it will help advance the state of the art in AD or FDI research for autonomous aerial vehicles and mobile robots to enhance the safety of autonomous and remote flight operations further. The dataset and the provided tools can be accessed from https://doi.org/10.1184/R1/12707963 .

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“…Recent developments in signal processing, pattern recognition, controllers and their systematic integration have attractive potentials for resolving numerous issues related to FDD. Currently, most studies carried out in FDD field focuses on the failure mechanism [1,2], feature extraction [3][4][5] and fault identification and decision-making [6,7].…”

Section: Introductionmentioning

confidence: 99%

Locating Sensors in Large-Scale Engineering Systems for Fault Isolation Based on Fault Feature Reduction

Wang

Smith

et al. 2020

Journal of the Franklin Institute

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Fault detection and diagnosis (FDD) modules in a modern control system are effective in detecting and identifying abnormal process behaviours in a timely manner, ensuring the high-performance of large-scale engineering systems. The detection and isolation of faults is essentially built on the characterisation of the observed behaviour of a system. However, due to the large number of technical indicators available for measurement, as well as the various constraints of sensor installation, monitoring all the operating parameters of a large-scale engineering system is not feasible. Therefore, locating sensors optimally in a large-scale system, to achieve a comprehensive description of an abnormality, becomes a key issue to successfully apply diagnostic technologies to real world situations. In this paper, a fault feature reduction (FFR) based sensor location approach is proposed for optimal sensor placement so as to achieve the desired performance of fault detection and isolation. The behaviour of faults is firstly analysed using a fault tree to obtain a comprehensive understanding of the multi-dimensional relationships between faults and symptoms. A Boolean matrix is then constructed to represent the corresponding relations around faults and potential sensors. All the alternative configurations of sensors, for a desired diagnosis of a system, are obtained by eliminating the redundant fault features. The trade-off without a certain sensor is also attained using the  * Corresponding author.

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Comparison of fault detection and isolation methods for a small unmanned aircraft

Cited by 30 publications

References 45 publications

Automatic Real-time Anomaly Detection for Autonomous Aerial Vehicles

Automatic Real-time Anomaly Detection for Autonomous Aerial Vehicles

ALFA: A dataset for UAV fault and anomaly detection

Locating Sensors in Large-Scale Engineering Systems for Fault Isolation Based on Fault Feature Reduction

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