no code implementations • 20 Jul 2023 • Tianfu Li, Chuang Suna, Ruqiang Yan, Xuefeng Chen, Olga Fink
To overcome these limitations, we propose two graph neural network models: the graph wavelet autoencoder (GWAE), and the graph wavelet variational autoencoder (GWVAE).
no code implementations • 4 Jul 2023 • Baorui Dai, Gaëtan Frusque, Tianfu Li, Qi Li, Olga Fink
We validate the effectiveness of the proposed SFDANN method based on two fault diagnosis cases: one involving fault diagnosis of bearings in noisy environments and another involving fault diagnosis of slab tracks in a train-track-bridge coupling vibration system, where the transfer task involves transferring from numerical simulations to field measurements.
no code implementations • 27 Mar 2023 • Tianfu Li, Chuang Sun, Olga Fink, Yuangui Yang, Xuefeng Chen, Ruqiang Yan
Intelligent fault diagnosis has been increasingly improved with the evolution of deep learning (DL) approaches.
3 code implementations • 6 Mar 2020 • Zhibin Zhao, Tianfu Li, Jingyao Wu, Chuang Sun, Shibin Wang, Ruqiang Yan, Xuefeng Chen
Second, we integrate the whole evaluation codes into a code library and release this code library to the public for better development of this field.
1 code implementation • 12 Nov 2019 • Tianfu Li, Zhibin Zhao, Chuang Sun, Li Cheng, Xuefeng Chen, Ruqiang Yan, Robert X. Gao
In this paper, a novel wavelet driven deep neural network termed as WaveletKernelNet (WKN) is presented, where a continuous wavelet convolutional (CWConv) layer is designed to replace the first convolutional layer of the standard CNN.