Review on online operation insulator identification and fault diagnosis based on UAV patrol images and deep learning algorithms

Zhang, Zhaoyun; Cai, Delong; Zhang, Zhi

doi:10.3389/fenrg.2022.912453

Cited by 4 publications

(4 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To train the classifier, the fight data of the quadrotor is utilized. The same as [21], our paper employs the angular accelerations ( ṗ, q, ṙ) and the square of the propeller rotational speed (ω 2 i=1,2,3,4 ) as input at step time t, denoted by x t as follows,…”

Section: Problem Descriptionmentioning

confidence: 99%

A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms

Zhang,

Miao,

Zhang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Accurate localization is the foundation of unmanned aerial vehicle (UAV) swarm applications in the global navigation satellite system (GNSS)-denied environment. However, the implementation of UAV formation in the real world is costly and time-consuming, which leads to difficulties in developing navigation algorithms. A real-time simulator for navigation in GNSS-denied environments is proposed, which includes world, model, controller, scene matching navigation (SMN), relative navigation and formation controller modules. Each module can be modified, which means that users can test their own algorithms. A novel inertial-aided SMN (ISMN) algorithm is developed and a relative navigation method that does not rely on inter communication is proposed. ISMN and relative navigation based on a camera and ultrawideband (UWB) are tested on the platform. Based on the developed simulation system, the navigation algorithms can be verified easily, which can reduce the time and personnel requirements during flight testing.

show abstract

Section: Problem Descriptionmentioning

confidence: 99%

A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms

Zhang,

Miao,

Zhang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Cahabug et al [12] proposed a failure detection system for a UAV that detects propeller failures to reduce the risk of crashes. Zhang et al [13] developed a simulation model to achieve high accuracy in detecting propeller faults in flight.…”

Section: Introductionmentioning

confidence: 99%

Comparison of VTOL UAV Battery Level for Propeller Faulty Classification Model

Mohd Sani,

Mohamad Zin,

Mohd Nor

et al. 2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

The degradation of batteries in UAVs may result in various problems, such as connectivity troubles, flight delays, and unexpected accidents. Flight safety and reliability are affected by propeller efficiency and performance. This study explores an acoustic-based method to classify propeller faulty conditions in Vertical Take-Off and Landing Unmanned Aerial Vehicles (VTOL UAV). The main objective is to emphasize the difference between classifier models developed using different battery-level flight data. The sound generated by VTOL UAV provides valuable information about the flight performance, essential for effectively monitoring flying conditions and identifying potential faults. This study uses three classification algorithms-Medium Tree (MT), Linear Support Vector Machine (LSVM), and Linear Discriminant (LD), to classify propeller failures of VTOL UAVs. Datasets are collected from three simulated propeller faulty conditions using a wireless microphone connected to a smartphone in an indoor lab environment with a soundproofing mechanism. The Mel Frequency Cepstral Coefficients technique is implemented in MATLAB (R2020a) to extract valuable features from the recorded sound signals. Extracted features from high and low-battery flights are utilized to develop classification models. Classifiers' performance is analyzed to compare the difference between selected models developed using high and low-battery flight data. The accuracy was measured with other samples to test the robustness of classification models. LSVM and MT classification models developed using high-battery flight data produce better accuracy than low-battery flight data in the training and testing phases. LD classification model developed using high-battery flight data produces better accuracy than low-battery flight data in the testing phase only. These results show that battery degradation can affect the performance of the VTOL UAV faulty classification algorithm.

show abstract

“…With the continuous development of deep learning technology, the integration of deep learning and power system has been widely promoted [11]. In 2019, Wu Tao et al [12] improved the traditional YOLOv3 network and proposed a method for detection of insulator damage based on the improved YOLOv3 algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on detection of insulator damage based on improved YOLOv4-tiny network

Lu,

Xu,

Jiang

et al. 2023

International Conference on Internet of Things and Machine Learning (IoTML 2023)

View full text Add to dashboard Cite

Review on online operation insulator identification and fault diagnosis based on UAV patrol images and deep learning algorithms

Cited by 4 publications

References 55 publications

A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms

A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms

Comparison of VTOL UAV Battery Level for Propeller Faulty Classification Model

Research on detection of insulator damage based on improved YOLOv4-tiny network

Contact Info

Product

Resources

About