Fault Diagnosis of Harmonic Reducers Based On a FDG-Resnet Joint Methodology-Reference-Cited by-同舟云学术

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Fault Diagnosis of Harmonic Reducers Based On a FDG-Resnet Joint Methodology

Published:2023-11-02 Issue: Volume: Page:1-7
ISSN:
Container-title:2023 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
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Author:

Lv Jing¹,Long Zhuo¹,Wang Juan¹,Ma Xiaoguang²

Affiliation:

1. College of Electronics and Information Engineering, Heilongjiang University of Science and Technology,Harbin,China

2. Engineering and also with the Foshan Graduate School, Northeastern University Northeastern University,Faculty of Robot Science,Foshan,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10490445/10490449/10490759.pdf?arnumber=10490759

Reference15 articles.

1. In-situ fault diagnosis for the harmonic reducer of industrial robots via multi-scale mixed convolutional neural networks

2. Signal estimation from modified short-time Fourier transform

3. On signal reconstruction from its spectrogram

4. Phase correction autocorrelation-based frequency estimation method for sinusoidal signal

5. Analysis of speech signals’ characteristics based on MF-DFA with moving overlapping windows

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