Gear fault diagnosis based on SGMD noise reduction and CNN
Author:
Affiliation:
1. School of Energy and Mechanical Engineering, Shanghai University of Electric Power
Publisher
Japan Society of Mechanical Engineers
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering
Link
https://www.jstage.jst.go.jp/article/jamdsm/16/3/16_2022jamdsm0031/_pdf
Reference24 articles.
1. Amarouayache, I.I.E., Saadi, M.N., Guersi, N. and Boutasseta, N., Bearing fault diagnosiss using EEMD processing and convolutional neural network methods, The International Journal of Advanced Manufacturing Technology, Vol. 107, No.9-10 (2020), pp.4077-4095.
2. Cheng, J., Yang, Y., Li, X. and Cheng, J.S., An early fault diagnosis method of gear based on improved symplectic geometry mode decomposition, Measurement, Vol. 151(2020), pp.107140.
3. Guo, J.F., Liu, X.Y., LI, S.X., Wang, Z.M. and Caesarendra, W., Bearing Intelligent Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network, Shock and Vibration, Vol. 2020(2020), pp.1-14.
4. Hu, N.Q., Chen, H.P., Cheng, Z., Zhang, L. and Zhang, Y., Fault Diagnosis for Planetary Gearbox Based on EMD and Deep Convolutional Neural Networks, Journal of Mechanical Engineering, Vol. 55, No.7(2019), pp.9-18.(In Chinese)
5. Krizhevsky, A., Sutskever, I. and E.Hinton, G., ImageNet Classification with Deep Convolutional Neural Networks, Communications of the ACM, Vol. 60, No.6 (2017), pp.84-90.
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