HOG-SVM-Based Image Feature Classification Method for Sound Recognition of Power Equipments

Kang Bai, Yong Zhou, Zhibo Cui, Weiwei Bao, Nan Zhang and Yongjie Zhai
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Kang Bai: Department of Automation, North China Electric Power University, Baoding 071003, China
Yong Zhou: SPIC Central Research Institute, Beijing 102209, China
Zhibo Cui: Department of Automation, North China Electric Power University, Baoding 071003, China
Weiwei Bao: SPIC Central Research Institute, Beijing 102209, China
Nan Zhang: SPIC Central Research Institute, Beijing 102209, China
Yongjie Zhai: Department of Automation, North China Electric Power University, Baoding 071003, China

Energies, 2022, vol. 15, issue 12, 1-12

Abstract: In this paper, a method of power system equipment recognition based on image processing is proposed. Firstly, we carry out wavelet transform on the sound signal of power system equipment collected from the site, and obtain the wavelet coefficient–time diagram. Then, the similarity of wavelet coefficients–time images of different equipment and the same equipment in different periods is calculated, which is used as the basis of the feasibility of image recognition. Finally, we select the HOG features of the image, and classify the selected features using SVM classifier. The method proposed in this paper can accurately identify and classify power system equipment through sound signals, and is different from the traditional method of classifying sound signals directly. The advantages of image processing can be effectively utilized through image processing to avoid the limitations of sound signal processing.

Keywords: electric power equipment; voice recognition; HOG feature extraction; SVM classifier; image processing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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