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

Author:

BUI Kien Huy1,IBA Daisuke1,ISHII Yunosuke1,TSUTSUI Yusuke1,MIURA Nanako1,IIZUKA Takashi1,MASUDA Arata1,SONE Akira1,MORIWAKI Ichiro1

Affiliation:

1. Kyoto Institute of Technology

Publisher

Japan Society of Mechanical Engineers

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering

Reference11 articles.

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2. Daisuke Iba, Shuhei Ohmori, Junichi Hongu, Morimasa Nakamura, Ichiro Moriwaki, Failure detection of plastic gears based on comparison of fourier coefficients of a gear mesh vibration model by rectangle pulse train and frequency analysis of acceleration response, Transactions of the Japan Society of Mechanical Engineers, Series C, Vol. 79, Issue 808 (2013), pp.5138-5148 (in Japanese).

3. Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Li Wang, Gang Wang, Jianfei Cai, Tsuhan Chen, Recent advances in convolutional neural networks. Pattern Recognition, Vol. 77 (2018), pp.354-377.

4. Karen Simonyan, Andrew Zisserman, Very deep convolutional networks for large-scale image recognition, Proceedings of International Conference on Learning Representation (ICLR) (2015), pp.1-14.

5. Kasthurirangan Gopalakrishnan, Siddhartha K. Khaitan, Alok Choudhary, Ankit Agrawal, Deep convolutional neural networks with transfer learning for computer vision-based data driven pavement distress detection, Journal of construction and building materials, Vol. 157 (2017), pp.322-330.

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