CHENG H B, WANG L, WU H,et al. Research on power equipment case aid diagnosis system based on deep learning[J]. Journal of Henan Polytechnic University(Natural Science) , 2025, 44(3): 130-137.

doi: 10.16186/j.cnki.1673-9787.2023040029

Received:2023/04/14

Revised :2023/09/28

Online:2025-04-18

Research on power equipment case aid diagnosis system based on deep learning

CHENG Hongbo1,2, WANG Lin1, WU Hao2, XIE Zining1, LI Haoling1

1.School of Electrical and Automation Engineering， East China Jiaotong University， Nanchang  330013， Jiangxi， China；2.Sichuan Key Laboratory of Artificial Intelligence， Yibin  643000， Sichuan， China

Abstract: Objectives In order to leverage the experience and knowledge of infrared diagnostic experts and provide a basis and reference for the diagnosis and treatment of electrical equipment faults. Methods a method for infrared image assisted diagnosis of power equipment was proposed. Typical fault cases of electrical equipment were collected and organized， and an infrared case library of typical faults of electrical equipment was established. By optimizing the number of fully connected layers in the network and using global average pooling instead of max pooling to improve the VGG-16 deep learning network， the number of intermediate features in infrared image processing was reduced， thereby reducing the computational workload of image matching. The improved VGG-16 network was used to extract typical features of infrared images， the cosine distance between the features of the test image and the typical case image was calculated， the similarity between the test image and the standard image was measured by their cosine distance， the closest similar case was selected to provide fault cause analysis and processing suggestions. 200 typical cases of 43 types of faults in 7 types of equipment were collected and an infrared assisted diagnosis program for power equipment was developed. Results The experimental results showed that using the improved deep learning network， single infrared image matching only took an average of 0.255 seconds， which was 85.5% shorter than that of the SURF method and 91.9% shorter than that of the SIFT method. After extracting features for matching， the diagnostic accuracy could reach 94.74%. Conclusions The proposed method could improve the efficiency of infrared image processing with high diagnostic accuracy， providing a new method for infrared diagnosis of power equipment. The diagnostic results could integrate existing expert experience and knowledge to provide guidance for on-site fault handling， which made it have good application potential.

Key words: deep learning; infrared detection; fault case; aid case diagnosis