TUO Y, WANG W, MAO H M, et al.Infrared intelligent diagnosis method of power equipment fault based on machine learning[J].Journal of Henan Polytechnic University(Natural Science) ,2022,41(5):121-126.

doi:10.16186/j.cnki.1673-9787.2020080063

Received:2020/08/25

Revised:2021/05/25

Published:2022/09/25

Infrared intelligent diagnosis method of power equipment fault based on 

machine learning

TUO Ya1, WANG Wei1, MAO Huamin2, CHENG Hongbo2, WANG Lin2

1.Inner Mongolia Electric Power Group Co.，Ltd.，Hohhot  010000，Inner Mongolia，China；2.School of Electrical and Automation Engineering， East China Jiaotong University，Nanchang  330013，Jiangxi，China

Abstract:Aiming at the problems of backward method and low efficiency of the power equipment infrared detection and diagnosis，the double-layer network was used to identify the equipment type and to divide the equipment structure，so as to realize the fast and effect diagnosis.Firstly，the R-FCN was used to establish the identification model of electrical equipment，and the Mask RCNN was used to segment the regional structure of power equipment.The maximum temperature of different regions was extracted automatically according to the divided structure，then the equipment status could be diagnosed by criteria according to the identified equipment type.The infrared intelligent diagnosis platform was built，and the experimental results showed that the method had high recognition accuracy，reliable state judgment results，moreover it did not need a large number of fault samples，which provided a fast and effective method for infrared diagnosis of electrical equipment.

Key words:power equipment;infrared detection;deep learning;intelligent diagnosis

 基于机器学习的电力设备故障红外智能诊断方法_托娅.pdf