Back to Search
Start Over
Using a Support Vector Machine Based Decision Stage to Improve the Fault Diagnosis on Gearboxes
- Source :
- Computational Intelligence and Neuroscience, Vol 2019 (2019), Computational Intelligence and Neuroscience
- Publication Year :
- 2019
- Publisher :
- Hindawi Limited, 2019.
-
Abstract
- Gearboxes are mechanical devices that play an essential role in several applications, e.g., the transmission of automotive vehicles. Their malfunctioning may result in economic losses and accidents, among others. The rise of powerful graphical processing units spreads the use of deep learning-based solutions to many problems, which includes the fault diagnosis on gearboxes. Those solutions usually require a significant amount of data, high computational power, and a long training process. The training of deep learning-based systems may not be feasible when GPUs are not available. This paper proposes a solution to reduce the training time of deep learning-based fault diagnosis systems without compromising their accuracy. The solution is based on the use of a decision stage to interpret all the probability outputs of a classifier whose output layer has the softmax activation function. Two classification algorithms were applied to perform the decision. We have reduced the training time by almost 80% without compromising the average accuracy of the fault diagnosis system.
- Subjects :
- Support Vector Machine
Article Subject
General Computer Science
Computer science
General Mathematics
Training time
Activation function
Decision Making
Automotive industry
02 engineering and technology
Machine learning
computer.software_genre
lcsh:Computer applications to medicine. Medical informatics
lcsh:RC321-571
0202 electrical engineering, electronic engineering, information engineering
Humans
lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry
business.industry
General Neuroscience
Deep learning
020208 electrical & electronic engineering
General Medicine
Support vector machine
Equipment Failure Analysis
Statistical classification
Softmax function
lcsh:R858-859.7
020201 artificial intelligence & image processing
Artificial intelligence
Neural Networks, Computer
business
computer
Classifier (UML)
Algorithms
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 16875273 and 16875265
- Volume :
- 2019
- Database :
- OpenAIRE
- Journal :
- Computational Intelligence and Neuroscience
- Accession number :
- edsair.doi.dedup.....76a8bf3b0d3c89b167a4ba087786e0f3