Vibration-based plastic-gear crack detection system using a convolutional neural network - Robust evaluation and performance improvement by re-learning

Vibration-based plastic-gear crack detection system using a convolutional neural network - Robust evaluation and performance improvement by re-learning

This paper evaluates the sensitivity of a proposed crack detection method of POM (Polyoxymethylene) gears using a deep convolutional neural network. The vibration signal was collected from an automatic data acquisition system for endurance tests of gears. The fast Fourier transform (FFT) of the meas...

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Bibliographic Details
Main Authors: Kien Huy BUI, Daisuke IBA, Yunosuke ISHII, Yusuke TSUTSUI, Nanako MIURA, Takashi IIZUKA, Arata MASUDA, Akira SONE, Ichiro MORIWAKI
Format: Article
Language:English
Published: The Japan Society of Mechanical Engineers 2020-03-01
Series:Journal of Advanced Mechanical Design, Systems, and Manufacturing
Subjects:
plastic gear
endurance test
vgg16 convnet
robustness
varied working condition
Online Access:https://www.jstage.jst.go.jp/article/jamdsm/14/3/14_2020jamdsm0035/_pdf/-char/en

Internet

https://www.jstage.jst.go.jp/article/jamdsm/14/3/14_2020jamdsm0035/_pdf/-char/en

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