Open AccessGearbox fault diagnosis based on transfer learning with RseNet50 model
Author(s) -
Chen He,
Yiqiang Jiang,
Li Sun,
Bin Wu
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 - 10.1088/1742-6596/1986/1/012093
Subject(s) - fault (geology) , convolutional neural network , transfer of learning , training set , computer science , test set , set (abstract data type) , artificial intelligence , data set , test data , artificial neural network , pattern recognition (psychology) , machine learning , seismology , geology , programming language
The fault diagnosis method based on ResNet50 convolutional neural network and migration learning is proposed as a way of improving the gearbox fault diagnosis. Firstly, exporting the normal and faulty data of the gearbox. Then converting the exported data into one-dimensional images to and generate the corresponding training and test sets, train in the model to get accuracy of the test set and training set. After fine adjustment, it is used for gearbox fault diagnosis, compared with the VGG16, ResNet101, and GoogleNet model, the accuracy of ResNet50 is above 86.6%. It has a good prospect of application, and its effect is obviously better than that of other models.