Real-Time UAV Fault Detection and Classification Using Measurement Data from the PADRE Database

Puchalski, Radosław; Giernacki, Wojciech; Ha, Quang

doi:10.1109/sii58957.2024.10417427

2024 IEEE/SICE International Symposium on System Integration (SII) 2024

DOI: 10.1109/sii58957.2024.10417427

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Real-Time UAV Fault Detection and Classification Using Measurement Data from the PADRE Database

Radosław Puchalski,

Wojciech Giernacki,

Quang Ha

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2024

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Cited by 2 publications

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PADRE – A Repository for Research on Fault Detection and Isolation of Unmanned Aerial Vehicle Propellers

Puchalski,

Ha,

Giernacki

et al. 2024

J Intell Robot Syst

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Unmanned aerial vehicles are being used increasingly in a variety of applications. They are more and more often operating in close proximity to people and equipment. This necessitates ensuring maximum stability and flight safety. A fundamental step to achieving this goal is timely and effective diagnosis of possible defects. Popular data-based methods require a large amount of data collected during flights in various conditions. This paper describes an open PADRE database of such measurements for the detection and classification of the most common faults - multirotor propeller failures. It presents the procedure of data acquisition, the structure of the repository and ways to use the various types of data contained therein. The repository enables research on drone fault detection to be undertaken without time-consuming preparation of measurement data. The database is available on GitHub at https://github.com/AeroLabPUT/UAV_measurement_data. The article also introduces new and universal quality indicators for evaluating classifiers with non-uniform parameters, are proposed. They allow comparison of methods tested for a variety of fault classes and with different processing times.

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PADRE – A Repository for Research on Fault Detection and Isolation of Unmanned Aerial Vehicle Propellers

Puchalski,

Ha,

Giernacki

et al. 2024

J Intell Robot Syst

View full text Add to dashboard Cite

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UAV propeller fault diagnosis using deep learning of non-traditional χ2-selected Taguchi method-tested Lempel–Ziv complexity and Teager–Kaiser energy features

Al-Haddad,

Giernacki,

Basem

et al. 2024

Sci Rep

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Fault detection and isolation in unmanned aerial vehicle (UAV) propellers are critical for operational safety and efficiency. Most existing fault diagnosis techniques rely basically on traditional statistical-based methods that necessitate better approaches. This study explores the application of untraditional feature extraction methodologies, namely Permutation Entropy (PE), Lempel–Ziv Complexity (LZC), and Teager–Kaiser Energy Operator (TKEO), on the PADRE dataset, which encapsulates various rotor fault configurations. The extracted features were subjected to a Chi-Square (χ2) feature selection process to identify the most significant features for input into a Deep Neural Network. The Taguchi method was utilized to test the performance of the recorded features, correspondingly. Performance metrics, including Accuracy, F1-Score, Precision, and Recall, were employed to evaluate the model’s effectiveness before and after the feature selection. The achieved accuracy has increased by 0.9% when compared with results utilizing traditional statistical methods. Comparative analysis with prior research reveals that the proposed untraditional features surpass traditional methods in diagnosing UAV propeller faults. It resulted in improved performance metrics with Accuracy, F1-Score, Precision, and Recall reaching 99.6%, 99.5%, 99.5%, and 99.5%, respectively. The results suggest promising directions for future research in UAV maintenance and safety protocols.

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Real-Time UAV Fault Detection and Classification Using Measurement Data from the PADRE Database

Cited by 2 publications

References 22 publications

PADRE – A Repository for Research on Fault Detection and Isolation of Unmanned Aerial Vehicle Propellers

PADRE – A Repository for Research on Fault Detection and Isolation of Unmanned Aerial Vehicle Propellers

UAV propeller fault diagnosis using deep learning of non-traditional χ2-selected Taguchi method-tested Lempel–Ziv complexity and Teager–Kaiser energy features

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