Thermographic Fault Diagnosis of Shaft of BLDC Motor

Głowacz, Adam

doi:10.3390/s22218537

Cited by 75 publications

(8 citation statements)

References 26 publications

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“…Therefore, we propose a local attention-guided Swin-transformer for thermal infrared sports object detection (LAGSwin) to address these limitations. In addition, our proposed thermal infrared moving target detection framework (LAGSwin) can be practically applied to thermal infrared imaging fault diagnosis [ 24 ] and other thermal infrared image target detection [ 25 ] tasks.…”

Section: Related Workmentioning

confidence: 99%

LAGSwin: Local attention guided Swin-transformer for thermal infrared sports object detection

Meng,

Si,

Mao

et al. 2024

PLoS ONE

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Compared with visible light images, thermal infrared images have poor resolution, low contrast, signal-to-noise ratio, blurred visual effects, and less information. Thermal infrared sports target detection methods relying on traditional convolutional networks capture the rich semantics in high-level features but blur the spatial details. The differences in physical information content and spatial distribution of high and low features are ignored, resulting in a mismatch between the region of interest and the target. To address these issues, we propose a local attention-guided Swin-transformer thermal infrared sports object detection method (LAGSwin) to encode sports objects’ spatial transformation and orientation information. On the one hand, Swin-transformer guided by local attention is adopted to enrich the semantic knowledge of low-level features by embedding local focus from high-level features and generating high-quality anchors while increasing the embedding of contextual information. On the other hand, an active rotation filter is employed to encode orientation information, resulting in orientation-sensitive and invariant features to reduce the inconsistency between classification and localization regression. A bidirectional criss-cross fusion strategy is adopted in the feature fusion stage to enable better interaction and embedding features of different resolutions. At last, the evaluation and verification of multiple open-source sports target datasets prove that the proposed LAGSwin detection framework has good robustness and generalization ability.

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Section: Related Workmentioning

confidence: 99%

LAGSwin: Local attention guided Swin-transformer for thermal infrared sports object detection

Meng,

Si,

Mao

et al. 2024

PLoS ONE

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“…The thermal imaging fault diagnosis technology can reflect the infrared thermal radiation image of the equipment through the thermal imaging sensor in real time, to determine the specific fault according to the change of colour gamut in the image. Based on thermal imaging, power of normalized image difference (PNID) [16] or the Common Part of the Arithmetic Mean of Thermographic Images (CPoAMoTI) [17] as the feature extraction method, combined with the neural network model, it completes the non-destructive detection of different types of brushless DC motors with high accuracy. Binarized Common Area of Image Differences (BCAoID) can further refine heat map features, to help the backpropagation neural network better optimize the parameters [18].…”

Section: Related Workmentioning

confidence: 99%

Live line strain clamp's DR image anomaly detection based on unsupervised learning

Haoliang

Jia

Yuting

et al. 2023

IET Generation Trans & Dist

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Due to the high‐risk working environment of high‐voltage transmission lines, defect samples of strain clamps cannot be fully and completely collected. As a result, the deep learning method based on defect sample tags cannot effectively identify all abnormalities. To solve this problem, an unsupervised anomaly detection method based on knowledge distillation is proposed, which only requires a small number of normal samples to drive the model for anomaly detection. ResNet is the framework of the teacherstudent model, and the feature activation layer after ResBlock is used for knowledge transfer. Residual‐assisted attention and pyramid‐splitting attention were used to enhance the spatial perception and multi‐scale information utilization ability of the model. This model only transmits the information of normal samples and is sensitive to abnormal samples. The proposed model outperformed the baseline by 23% and individual categories by 78% on the MVTec AD (Anomaly Detection Dataset) and outperformed the baseline by 45% and individual categories by 10% on the CIFAR10 and is also reliable for Mnist and Fashion Mnist. This method performs best (82.71%) over the existing method on the self‐built data set.

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“…One of the biggest weaknesses of conventional brushed DC motors is their maintenance. Because of the friction caused by brushes, classic DC motors require the periodic exchange of certain components [7,8]. Comparing both constructions, it is unquestionable that the brushless DC (BLDC) motor has a simplified internal structure.…”

Section: Introductionmentioning

confidence: 99%

Stable Rules Definition for Fuzzy TS Speed Controller Implemented for BLDC Motor

Kaczmarczyk,

Malarczyk,

Ferreira

et al. 2024

Applied Sciences

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This paper deals with the application of a fuzzy-logic-based controller for a drive with a BLDC motor. Two main aspects of the work are presented in this paper. The first of them is focused on the design process of the fuzzy model. For this purpose, the rules of the applied fuzzy system are defined according to the Lyapunov function. The gain coefficients of the controller are optimized using the Chameleon Swarm Algorithm. Various issues and aspects were analyzed in the simulation tests (robustness against parameter changes, the influence of the controller parameters on the precision of control, stages of optimization, etc.). The presented work confirms the assumptions regarding precision and stable operation (also in the presence of changes in the object parameters—the mechanical time constants of the engine) in connection with the applied fuzzy speed controller. Moreover, the second part of the manuscript presents the low-cost power electronic device developed for laboratory tests. It uses STM32 ARM-based microcontrollers (state variable measurement, control algorithm calculation, control signal generation) and a Raspberry Pi 4B microcomputer (control application—reference value and parameter definition). The experiment performed for the control structure—an electric drive with a BLDC motor—verified the theoretical considerations.

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Thermographic Fault Diagnosis of Shaft of BLDC Motor

Cited by 75 publications

References 26 publications

LAGSwin: Local attention guided Swin-transformer for thermal infrared sports object detection

LAGSwin: Local attention guided Swin-transformer for thermal infrared sports object detection

Live line strain clamp's DR image anomaly detection based on unsupervised learning

Stable Rules Definition for Fuzzy TS Speed Controller Implemented for BLDC Motor

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