UAV Abnormal State Detection Model Based on Timestamp Slice and Multi-Separable CNN

Yang, Tao; Chen, Jiangchuan; Deng, Hucheng; Lu, Yu

doi:10.3390/electronics12061299

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…These studies relied on open-source libraries and APIs to receive flight data and control the UAVs. A SAS should be embedded as an additional safety module for all types of GCS for UAV safety [11].…”

Section: Uav Alerting Systemmentioning

confidence: 99%

Design of Alerting System for Beyond Visual Line of Sight Operational Cargo Delivery UAV

Sonaria,

Jenie

2024

war. ardhia

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The development of drones is now crucial to many industries, investors, and governments as they are more cost-effective and efficient in various industries, such as filmmaking, consumer, and tourism. They can also be used in various fields, such as agriculture, meteorology, construction, logistics, and conservation. Beyond Visual Line of Sight (BVLOS) technology enables drone operations to perform missions more accurately as it allows them to be operated in a wider angular range. BVLOS includes the use of advanced technologies and systems to monitor drones and ensure they comply with regulations. As such, BVLOS can be used to optimise marine cargo drone operations. In addition, the Ground Control System (GCS) is used by multi-UAV systems to remotely monitor drone performance. This enables advancements in navigation and autonomous technologies that can be utilised in the maritime sector. However, the operation of cargo delivery UAVs, especially those operated as multi-UAVs, requires a surveillance system supported by a qualified warning system. One of them is a warning system that will appear when the UAV approaches the boundary of the area of operation that has been set at the beginning. The warning system aims to improve safety and security because by warning UAVs approaching or entering restricted or sensitive areas, this system can prevent unauthorised access or accidents, ensuring the safety and security of the area and the UAV itself. In addition, it improves efficiency and reduces costs where the warning system can help UAVs avoid entering prohibited or unauthorised locations, improving efficiency, and reducing costs associated with re-routing or returning to home. For this reason, in this research, the design of a warning system for UAVs approaching the boundary of the operating area is performed and shown through simulation. In this research, a two-level alert system was designed and simulated that is triggered when the UAV approaches the boundary of the specified operation area to enable the operation supervisor to perform safety procedures in response to mitigate potential risks.

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Section: Uav Alerting Systemmentioning

confidence: 99%

Design of Alerting System for Beyond Visual Line of Sight Operational Cargo Delivery UAV

Sonaria,

Jenie

2024

war. ardhia

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“…Some choose to combine optimization algorithms like PSO and Genetic Algorithms (GA) to address convergence problems during weight training. When considering the selection of input parameters, the vast majority of studies still rely on the research experience of their predecessors, without considering the impact of different input parameter combinations on the output results [24][25][26][27]. Additionally, these network models yield fixed weight matrices after training, and these weight matrices are no longer updated.…”

Section: Related Workmentioning

confidence: 99%

A Data-Driven Approach Using Enhanced Bayesian-LSTM Deep Neural Networks for Picks Wear State Recognition

Song,

Zhao

2023

Electronics

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Picks are key components for the mechanized excavation of coal by mining machinery, with their wear state directly influencing the efficiency of the mining equipment. In response to the difficulty of determining the overall wear state of picks during coal-mining production, a data-driven wear state identification model for picks has been constructed through the enhanced optimization of Long Short-Term Memory (LSTM) networks via Bayesian algorithms. Initially, a mechanical model of pick and coal-rock interaction is established through theoretical analysis, where the stress characteristic of the pick is analyzed, and the wear mechanism of the pick is preliminarily revealed. A method is proposed that categorizes the overall wear state of picks into three types based on the statistical relation of the actual wear amount and the limited wear amount. Subsequently, the vibration signals of the cutting drum from a bolter miner that contain the wear information of picks are decomposed and denoised using wavelet packet decomposition, with the standard deviation of wavelet packet coefficients from decomposed signal nodes selected as the feature signals. These feature signals are normalized and then used to construct a feature matrix representing the vibration signals. Finally, this constructed feature matrix and classification labels are fed into the Bayesian-LSTM network for training, thus resulting in the picks wear state identification model. To validate the effectiveness of the Bayesian-LSTM deep learning algorithm in identifying the overall picks wear state of mining machinery, vibration signals from the X, Y, and Z axes of the cutting drum from a bolter miner at the C coal mine in Shaanxi, China, are collected, effectively processed, and then input into deep LSTM and Back-Propagation (BP) neural networks respectively for comparison. The results showed that the Bayesian-LSTM network achieved a recognition accuracy of 98.33% for picks wear state, showing a clear advantage over LSTM, BP network models, thus providing important references for the identification of picks wear state based on deep learning algorithms. This method only requires the processing and analysis of the equipment parameters automatically collected from bolter miners or other mining equipment, offering the advantages of simplicity, low cost, and high accuracy, and providing a basis for a proper picks replacement strategy.

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“…The experimental results show that the proposed method has a good effect on detecting structural abnormalities of UAVs. Yang et al [30] proposed a method for detecting anomalous states in UAVs using timestamp slicing and multiple separable convolutional neural networks (TS-MSCNN) and conducted experiments with real data. This method solves the problem that the traditional abnormal state detection model ignores the difference of POS data frequency domain in the process of feature learning.…”

Section: Introductionmentioning

confidence: 99%

An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder

Zhou,

He,

Chen

et al. 2024

Aerospace

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The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy of the proposed anomaly detection method with wavelet decomposition and stacked denoising autoencoder methods. Anomaly detection based on UAV flight data is an important method of UAV condition monitoring and potential abnormal state mining, which is an important means to reduce the risk of UAV flight accidents. However, the diversity of UAV mission scenarios leads to a complex and harsh environment, so the acquired data are affected by noise, which brings challenges to accurate anomaly detection based on UAV data. Firstly, we use wavelet decomposition to denoise the original data; then, we used the stacked denoising autoencoder to achieve feature extraction. Finally, the softmax classifier is used to realize the anomaly detection of UAV. The experimental results demonstrate that the proposed method still has good performance in the case of noisy data. Specifically, the Accuracy reaches 97.53%, the Precision is 97.50%, the Recall is 91.81%, and the F1-score is 94.57%. Furthermore, the proposed method outperforms the four comparison models with more outstanding performance. Therefore, it has significant potential in reducing UAV flight accidents and enhancing operational safety.

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UAV Abnormal State Detection Model Based on Timestamp Slice and Multi-Separable CNN

Cited by 7 publications

References 23 publications

Design of Alerting System for Beyond Visual Line of Sight Operational Cargo Delivery UAV

Design of Alerting System for Beyond Visual Line of Sight Operational Cargo Delivery UAV

A Data-Driven Approach Using Enhanced Bayesian-LSTM Deep Neural Networks for Picks Wear State Recognition

An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder

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