Statistical Feature Analysis of Thermal Images from Electrical Equipment

Dutta, Tamal; Santra, Deepjyoti; Peng-Lim, Chee; Sil, Jaya; Chottopadhyay, Paramita

doi:10.1007/978-981-13-0860-4_10

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…In order to verify the relationship between the informative parameters of active thermal testing and defect parameters, Pearson correlation coefficients for received datasets were determined [10]. Algorithms for determining the correlation coefficient and checking the significance of obtained data implemented in the MATLAB software.…”

Section: Correlation Analysismentioning

confidence: 99%

Statistical Analysis of Thermal Nondestructive Testing Data

Galagan

Momot

2019

A.I.S.

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The features of processing of active thermal nondestructive testing results are considered. Proved the necessity of search and introduction of new informative parameters in evaluation of thermograms in order to improve the reliability of control. Task of detecting and estimating the relationships between defect parameters and optimal testing time and the maximum value of temperature signal is set. Computer simulation of active thermal testing of two samples with artificial defects with known characteristics was performed. Also obtained the sequences of thermograms and formed the sets of initial data during simulation for correlation, regression and dispersion analysis of testing results. The method of dynamic thermal tomography was used to determine the levels of maximum differential temperature signal and optimal testing time. The estimates of correlation coefficient for various informational parameters of thermal control obtained. There is a high level of relations between the optimal control time and depth of defects. A high correlation also observed between the maximum value of temperature signal and depth of defects. The nature of relationships between various informative parameters of active thermal control established by the regression analysis. A one-factor dispersion analysis of the influence of defect parameters on optimal testing time and maximum value of the temperature signal was performed. High degree of mutual influence of all informative parameters is established. The conclusion made on the necessity of developing new modern methods for analysis the data of thermal testing. Revealed patterns in relationships between data show low efficiency of traditional statistical methods in tasks of active thermal testing. Alternatively, proposed to use the artificial intelligence technologies, in particular, neural networks.

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Section: Correlation Analysismentioning

confidence: 99%

Statistical Analysis of Thermal Nondestructive Testing Data

Galagan

Momot

2019

A.I.S.

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A Deep Learning-Based Fault Diagnosis Approach for Power System Equipment via Infrared Image Sensing

Liu,

Liu

2024

J CIRCUIT SYST COMP

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Automatic fault diagnosis for power system equipment has always been an essential concern in this industry. Conventionally, such works are conducted by manual patrol inspection, which consumes much human labor and expert knowledge. Fortunately, infrared images can present diagnosis areas inside the equipment via the thermal sensing function. In such context, this work utilizes deep neural network to construct a specific infrared image processing framework that can realize automatic fault diagnosis. Thus in this paper, a deep learning-based fault diagnosis approach for power system equipment via infrared image sensing is proposed. First, a pulse-coupled neural network structure is employed to enhance feature representation for infrared images of the equipment. Next, a fuzzy [Formula: see text]-means (FCM)-based segmentation method is developed to filter diagnosis areas from the infrared images. Finally, a convolution operation-based fault diagnosis operator is adopted to identify the diagnosis types. After that, some simulation experiments are conducted on real-world infrared images on the power system equipment, in order to make the performance evaluation of the proposed approach. The proposal realizes the end-to-end process of feature extraction and fault detection and identification, and avoids the problems of single feature. It is due to manual extraction of fault features, and the inability to detect and identify faults effectively in specific situations and scenarios.

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Statistical Feature Analysis of Thermal Images from Electrical Equipment

Cited by 2 publications

References 12 publications

Statistical Analysis of Thermal Nondestructive Testing Data

Statistical Analysis of Thermal Nondestructive Testing Data

A Deep Learning-Based Fault Diagnosis Approach for Power System Equipment via Infrared Image Sensing

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