A Data-Driven-Based Fault Diagnosis Approach for Electrical Power DC-DC Inverter by Using Modified Convolutional Neural Network With Global Average Pooling and 2-D Feature Image

A Data-Driven-Based Fault Diagnosis Approach for Electrical Power DC-DC Inverter by Using Modified Convolutional Neural Network With Global Average Pooling and 2-D Feature Image

A novel convolutional neural network namely the modified CNN-GAP model is proposed for fast fault diagnosis of the DC-DC inverter. This method improves the model structure of the traditional CNN by using a global average pooling layer to replace the fully connected layer of 2~3 layers. The improved...

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Bibliographic Details
Main Authors: Wenfeng Gong, Hui Chen, Zehui Zhang, Meiling Zhang, Haibo Gao
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Intelligent fault diagnosis
data-driven
convolutional neural network
global average pooling
2-D feature image
deep learning
Online Access:https://ieeexplore.ieee.org/document/9069268/

Internet

https://ieeexplore.ieee.org/document/9069268/

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