Detecting Contextual Faults in Unmanned Aerial Vehicles Using Dynamic Linear Regression and K-Nearest Neighbour Classifier

Alos, A.; Dahrouj, Zouhair

doi:10.1134/s2075108720010046

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…The following list of UAV-related applications has used linear regression. Alos et al introduced a novel method in [113] for contextual fault detection in UAV systems that makes use of the intricate linear correlations between UAV properties, such as sensor data and orders. An UAV system is a complicated system because of the control, aerodynamics, and communication systems that go into its design.…”

Section: E Classical Machine Learning Algorithmsmentioning

confidence: 99%

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A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

Sai,

Garg,

Jhawar

et al. 2023

IEEE Open J. Veh. Technol.

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Artificial Intelligence(AI) is an emerging technology that finds its application in various industries. Integration of AI in Unmanned Aerial Vehicles(UAVs) can lead to tremendous growth in the field of UAVs by improving flight safety and efficiency. Machine learning algorithms can enable UAVs to make real-time decisions in complex environments and reach the optimal solution that aims to fulfill a mission's requirements within the hardware constraints such as battery and payload. Several recent works in UAVs employed a variety of machine learning algorithms to enhance the capabilities of UAVs and assist them. Although several reviews have been published examining the various aspects of AI for UAVs, they are all pertaining to particular applications or technologies. Addressing this research gap, we present a comprehensive and diversified review to enable researchers to analyze the current and future requirements and develop the latest solutions utilizing AI. We have classified the reviewed works based on three different classification schemes: 1) application scenario-based, 2) AI algorithm-based, and 3) AI training paradigm-based. We have also presented a compilation of frameworks, tools, and libraries used in AI-integrated UAV systems. We identified that the integration of AI in UAVs has a wide array of applications ranging from path planning to resource allocation. We have observed that Reinforcement Learning based algorithms are more often used in AI-integrated UAV systems than other AI algorithms. Further, our findings reveal that UAV frameworks employing federated learning and other distributed machine learning paradigms are quickly emerging. Furthermore, we also have put forth several challenges and potential applications of AI-integrated UAV systems.

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Section: E Classical Machine Learning Algorithmsmentioning

confidence: 99%

“…A multi-stage experimental design to assess the system's resilience and prediction accuracy shows how useful this technique is. The research by Alos et al [113] suggests a novel method for identifying contextual errors in UAV systems. Control, aerodynamics, and communication systems are all used in UAV design.…”

Section: E Classical Machine Learning Algorithmsmentioning

confidence: 99%

A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

Sai,

Garg,

Jhawar

et al. 2023

IEEE Open J. Veh. Technol.

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“…Because computer control systems collect a large amount of process data, data-driven fault diagnosis algorithms are paid increasingly attentions. 1 In recent decades, a variety of data-driven fault diagnosis methods have been extensively studied, including the K-nearest neighbor classifier, 2 support vector machine, 3 fisher discriminant analysis, 4 and random forest. 5 Most of the methods mentioned above are applied to fault diagnosis scenarios with a small amount of data.…”

Section: Introductionmentioning

confidence: 99%

“…This extra information increases the sensitivity of the model to the data directionality. (2) The fault diagnosis methods are presented on the basis of the new multi-head attention mechanism, for the scenarios with and without missing data. For the fault diagnosis scenarios with missing data, by using the interpretability of attention weight matrix, a special attention weight modified method for missing data is designed to reduce the influence of missing data on fault diagnosis results.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method based on the multi-head attention focusing on data positional information

Feng

Zhao

Wang

et al. 2022

Measurement and Control

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In order to make full use of the absolute position information of fault signal, this paper designs a new multi-head attention (MHA) mechanism focusing on data positional information, proposes a novel MHA-based fault diagnosis method and extends it to the fault diagnosis scenario with missing information. Based on the absolute positional information and the trainable parameter matrix of the fault data, a novel attention weight matrix is generated, and the fault features are extracted by a fully connected network with the attention mechanism. By integrating the positional information into the weight matrix, the new MHA mechanism has the ability to extract more effective data features, compared with the traditional MHA method. Furthermore, the proposed method is also developed for the fault diagnosis scenarios with missing information. A special attention weight modified method is designed to reduce the impact of missing data on fault diagnosis results. In the experiment simulations, the data sampled from ZHS-2 multi-function motor flexible rotor test bed and the Tennessee- Eastman process data are utilized to test the performance of the algorithm. The results show that the proposed method can effectively extract fault features and reduce the impact of missing data.

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