Open AccessInfrared-Visual Image Fusion and CNN Model in Electrical Faults Diagnosis
Author(s) -
Lei Su,
Qi Ni,
Qiao Jin,
Boyuan Cao,
Zhaohong Xu,
Xiaorui Yu,
Dai Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1885/4/042068
Subject(s) - artificial intelligence , computer science , fuse (electrical) , convolution (computer science) , computer vision , convolutional neural network , image fusion , electrical equipment , wavelet , interference (communication) , pattern recognition (psychology) , layer (electronics) , artificial neural network , image (mathematics) , engineering , channel (broadcasting) , telecommunications , materials science , mechanical engineering , electrical engineering , composite material
In this paper, we proposed a new faults diagnosis method based on the fusion of visible and infrared images of electrical equipment. Firstly, the discrete wavelet transform method is used to fuse the visible and infrared images, which enables the accurate location of the electrical equipment. Then, a deep convolution neural network (CNN) model is employed to identify faults in electrical equipment. Three parameters of CNN including connection weight, convolution layer parameters and pooling layer strategy are designed in this paper. The inputs of CNN are the fused reconstructed images and the outputs are the classifications of faults. Finally, simulation experiments and analysis show that the algorithm proposed in this paper can effectively improve the contrast and clarity of the fused images. It can reduce noise interference, and improve the location accuracy of electrical equipment. More specifically, the faults diagnosis rate is improved by 2-6% with the proposed method.