Fault Diagnosis of Power Equipment Based on Infrared Image Analysis

Guo, Chenxi; Ren, Ming; Xia, Changjie; Dong, Ming; Wang, Bin

doi:10.1109/aeeca49918.2020.9213457

Cited by 5 publications

(6 citation statements)

References 8 publications

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“…The four performance indicators of precision, recall, accuracy, and TOPSIS of the insulator infrared fault-diagnosis method proposed in this paper are better than other methods, which are 0.984, 0.988, 0.972, and 0.873, respectively. From the error bar in Figure 14, it is found that the method proposed in this paper has the smallest error of these four indicators, which further shows that the ARG-Mask RCNN method has the best performance in the infrared insulator fault-diagnosis method [51]. From the error bar in Figure 14, it is found that the method proposed in this paper has the smallest error of these four indicators, which further shows that the ARG-Mask RCNN method has the best performance in the infrared insulator fault-diagnosis method [51].…”

Section: Arg-mask Rcnn Performance Testmentioning

confidence: 69%

“…The vertical axis represents the scores under different indicators of each method. The four performance indicators of precision, recall, accuracy, and TOPSIS of the insulator infrared fault-diagnosis method proposed in this paper are better than other methods, which are 0.984, 0.988, 0.972, and 0.873, respectively.From the error bar in Figure14, it is found that the method proposed in this paper has the smallest error of these four indicators, which further shows that the ARG-Mask RCNN method has the best performance in the infrared insulator fault-diagnosis method[51].…”

mentioning

confidence: 67%

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ARG-Mask RCNN: An Infrared Insulator Fault-Detection Network Based on Improved Mask RCNN

Zhou¹,

Wang²,

Li³

2022

Sensors

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Traditional power equipment defect-detection relies on manual verification, which places a high demand on the verifier’s experience, as well as a high workload and low efficiency, which can lead to false detection and missed detection. The Mask of the regions with CNN features (Mask RCNN) deep learning model is used to provide a defect-detection approach based on the Mask RCNN of Attention, Rotation, Genetic algorithm (ARG-Mask RCNN), which employs infrared imaging as the data source to assess the features of damaged insulators. For the backbone network of Mask RCNN, the structure of Residual Network 101 (ResNet101) is improved and the attention mechanism is added, which makes the model more alert to small targets and can quickly identify the location of small targets, improve the loss function, integrate the rotation mechanism into the loss function formula, and generate an anchor frame where a rotation angle is used to accurately locate the fault location. The initial hyperparameters of the network are improved, and the Genetic Algorithm Combined with Gradient Descent (GA-GD) algorithm is used to optimize the model hyperparameters, so that the model training results are as close to the global best as possible. The experimental results show that the average accuracy of the insulator fault-detection method proposed in this paper is as high as 98%, and the number of frames per second (FPS) is 5.75, which provides a guarantee of the safe, stable, and reliable operation of our country’s power system.

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Section: Arg-mask Rcnn Performance Testmentioning

confidence: 69%

mentioning

confidence: 67%

ARG-Mask RCNN: An Infrared Insulator Fault-Detection Network Based on Improved Mask RCNN

Zhou¹,

Wang²,

Li³

2022

Sensors

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“…Guo et al proposed a diagnosis system based on the comprehensive analysis of infrared images. This system uses the Sobel operator and Canny operator for preprocessing, the SIFT algorithm extracts prefeature points, and the K -means clustering identifies power equipment [ 6 ]. With the development of deep learning, deep learning has been applied to more and more tasks.…”

Section: Introductionmentioning

confidence: 99%

MSIS: Multispectral Instance Segmentation Method for Power Equipment

Shu

2022

Computational Intelligence and Neuroscience

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Infrared image of power equipment is widely used in power equipment fault detection, and segmentation of infrared images is an important step in power equipment thermal fault detection. Nevertheless, since the overlap of the equipment, the complex background, and the low contrast of the infrared image, the current method still cannot complete the detection and segmentation of the power equipment well. To better segment the power equipment in the infrared image, in this paper, a multispectral instance segmentation (MSIS) based on SOLOv2 is designed, which is an end-to-end and single-stage network. First, we provide a novel structure of multispectral feature extraction, which can simultaneously obtain rich features in visible images and infrared images. Secondly, a module of feature fusion (MARFN) has been constructed to fully obtain fusion features. Finally, the combination of multispectral feature extraction, the module of feature fusion (MARFN), and instance segmentation (SOLOv2) realize multispectral instance segmentation of power equipment. The experimental results show that the proposed MSIS model has an excellent performance in the instance segmentation of power equipment. The MSIS based on ResNet-50 has 40.06% AP.

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“…With the continuous increase of power equipment, traditional manual-line inspection and substation monitoring have been difficult to meet the actual requirements. In this context, a large number of power-line inspection equipment types based on helicopters, drones, and other computational platforms have been put into application [1][2][3][4][5]. ese devices collect images of power equipment through optical and infrared sensors on themselves or nearby.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Power Equipment Based on Multitask Sparse Representation

Lei¹,

Wu²,

Zheng

et al. 2021

Scientific Programming

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Image analysis of power equipment has important practical significance for power-line inspection and maintenance. This paper proposes an image recognition method for power equipment based on multitask sparse representation. In the feature extraction stage, based on the two-dimensional (2D) random projection algorithm, multiple projection matrices are constructed to obtain the multilevel features of the image. In the classification process, considering that the image acquisition process will inevitably be affected by factors such as light conditions and noise interference, the proposed method uses the multitask compressive sensing algorithm (MtCS) to jointly represent multiple feature vectors to improve the accuracy and robustness of reconstruction. In the experiment, the images of three types of typical power equipment of insulators, transformers, and circuit breakers are classified. The correct recognition rate of the proposed method reaches 94.32%. In addition, the proposed method can maintain strong robustness under the conditions of noise interference and partial occlusion, which further verifies its effectiveness.

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Fault Diagnosis of Power Equipment Based on Infrared Image Analysis

Cited by 5 publications

References 8 publications

ARG-Mask RCNN: An Infrared Insulator Fault-Detection Network Based on Improved Mask RCNN

ARG-Mask RCNN: An Infrared Insulator Fault-Detection Network Based on Improved Mask RCNN

MSIS: Multispectral Instance Segmentation Method for Power Equipment

Recognition of Power Equipment Based on Multitask Sparse Representation

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