Uncertainty quantification in deep convolutional neural network diagnostics of journal bearings with ovalization fault

2020 ◽  
Vol 149 ◽  
pp. 103835 ◽  
Author(s):  
Diogo Stuani Alves ◽  
Gregory Bregion Daniel ◽  
Helio Fiori de Castro ◽  
Tiago Henrique Machado ◽  
Katia Lucchesi Cavalca ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Uncertainty Quantification ◽  
Journal Bearings ◽  
Deep Convolutional Neural Network

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

Sign in / Sign up

Export Citation Format

Share Document