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Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved Deep Residual Network
- Source :
- Applied Sciences, Volume 11, Issue 22, Applied Sciences, Vol 11, Iss 10889, p 10889 (2021)
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- Rolling bearings are the most fault-prone parts in rotating machinery. In order to find faults in time and reduce losses, this paper presents an intelligent diagnosis method for rolling bearings. At present, the deep residual network (RESNET) is the most widely used convolutional neural network (CNN) and has become one of the hotspots in fault diagnosis. However, the fully connected layer of the deep residual network has the disadvantage of too many training parameters, which makes the model training and testing time longer. So, we proposed a new network structure which the global average pooling (GAP) technology replaces the fully connected layer part of the traditional RESNET. It effectively solves the problem of too many parameters of the traditional RESNET model, and uses data enhancement, dropout, and other deep learning training techniques to prevent the model from overfitting. Experiments show that the accuracy of fault diagnosis of the improved algorithm reaches 99.83%, training time has been shortened. Also, the whole process of rolling bearing fault detection does not need any manually extract features, and this “end-to-end” algorithm has good versatility and operability.
- Subjects :
- Technology
Operability
QH301-705.5
Computer science
QC1-999
Real-time computing
Overfitting
Residual
Fault (power engineering)
Convolutional neural network
law.invention
law
General Materials Science
Biology (General)
QD1-999
Instrumentation
Dropout (neural networks)
Fluid Flow and Transfer Processes
Bearing (mechanical)
business.industry
Physics
Process Chemistry and Technology
Deep learning
General Engineering
deep learning
GAP
fault diagnosis
Engineering (General). Civil engineering (General)
Computer Science Applications
Chemistry
Artificial intelligence
TA1-2040
business
improved deep residual network
Subjects
Details
- ISSN :
- 20763417
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....82b886e7791caf45bccc78164ec40903
- Full Text :
- https://doi.org/10.3390/app112210889