Condition monitoring of electrical equipment using thermal image processing

Dutta, Tamal; Sil, Jaya; Chottopadhyay, Paramita

doi:10.1109/cmi.2016.7413761

Cited by 37 publications

(8 citation statements)

References 17 publications

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“…Dutta et al 10 studied the image processing technology in thermal state monitoring of power equipment. After converting RGB color model to HSI color model, they processed it and extracted different gradient edge descriptors.…”

Section: Intelligent Monitoring Systemmentioning

confidence: 99%

Detection of abnormal dangerous behavior in electrical power grids based on the fusion of contextual features

Huang

Gao

Qiu

et al. 2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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Intelligence inspection of the electrical power grid can effectively improve the efficiency of inspection. However, the complex scenes between grid work and the interference of similar objects seriously affect the recognition accuracy of target identification. To address the above problems, this paper proposes the detection of abnormal dangerous behaviors in power grids based on contextual feature fusion. First, the feature extraction backbone network is constructed to obtain image features. Second, the hierarchical contextual attention mechanism is constructed to capture contextual features. Finally, the target detection model with contextual feature fusion is constructed to achieve grid abnormal risk behavior detection. The model proposed in this article is compared with the existing object recognition model in the simulation of the three datasets of Safety helmet (hardhat) wearing detect dataset, Hard Hat Workers dataset, Safety Helmet Detection dataset. A large number of experiments have proved the object recognition algorithm proposed in this paper is effective and outperforms existing algorithms. The average recognition accuracy of the proposed model is 0.948, which is improved by 1.02%.

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Section: Intelligent Monitoring Systemmentioning

confidence: 99%

Detection of abnormal dangerous behavior in electrical power grids based on the fusion of contextual features

Huang

Gao

Qiu

et al. 2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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“…Transformer bushing [44] Bi-threshold OTSU algorithm Insulator [8] Mathematical morphology improved OTSU algorithm Insulator [45] Control components for electric machines [46] Edge detection method Roberts operator Solar panel connecter [47,48] Prewitt operator…”

Section: Model and Algorithm Object Referencementioning

confidence: 99%

Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐art

et al. 2020

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As a non‐contact temperature distribution measurement method, infrared thermography (IRT) has emerged as an indispensable tool in condition monitoring and fault diagnosis of electrical equipment based on the absolute and relative temperature values. Manual fault inspection, as an expert‐experiences based evaluation method, has formed a mature technical scheme with a large number of application cases. However, the efficiency and accuracy of manual fault inspection are being challenged by the rapid growth in the number of equipment in power grid. The situation is improving with the advanced of image processing technique. Machine‐assisted fault diagnosis provides a novel method to assist human beings to complete fault diagnosis under the intervention of human prior knowledge. However, the limitations of infrared images bring challenges to image analysis processing especially target detection. In pursuit of automatic fault diagnosis, deep learning algorithms are introduced to achieve target detection in the complex environment. This study reviews the development of IRT‐based diagnostics beginning with the general procedures, objects, and limitations of IRT‐based fault inspection, and then gives an insight into the popular machine‐assisted fault diagnosis as well as image‐based intelligent fault identification. In addition, the future recommendations of IRT are also provided from construction of intelligent infrared detection system, establishment of an open and shared infrared image database and comprehensive utilization of joint visualization diagnosis technology.

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“…Therefore, the new studies are required to look for ways to adapt to a new colour model for processing the thermal image. For this reason, the Hue, Saturation and Value (HSV) colour model has been applied in some condition monitoring applications such as monitoring the electrical equipment as in [23]. As a result, in this paper the thermal image was converted to HSV colour model rather than into grayscale for induction motor fault detection.…”

Section: Introductionmentioning

confidence: 99%