1 code implementation • 11 Apr 2024 • Jing-Xiao Liao, Chao He, Jipu Li, Jinwei Sun, Shiping Zhang, Xiaoge Zhang
Blind deconvolution (BD) has been demonstrated as an efficacious approach for extracting bearing fault-specific features from vibration signals under strong background noise.
1 code implementation • 21 Sep 2023 • Wei-En Yu, Jinwei Sun, Shiping Zhang, Xiaoge Zhang, Jing-Xiao Liao
In this paper, we propose a supervised contrastive learning approach with a class-aware loss function to enhance the feature extraction capability of neural networks for fault diagnosis.
1 code implementation • 31 Jul 2023 • Jing-Xiao Liao, Sheng-Lai Wei, Chen-Long Xie, Tieyong Zeng, Jinwei Sun, Shiping Zhang, Xiaoge Zhang, Feng-Lei Fan
To the best of our knowledge, this is the first instance of deploying a CNN-based bearing fault diagnosis model on an FPGA.
1 code implementation • 1 Jun 2022 • Jing-Xiao Liao, Hang-Cheng Dong, Zhi-Qi Sun, Jinwei Sun, Shiping Zhang, Feng-Lei Fan
Bearing fault diagnosis is of great importance to decrease the damage risk of rotating machines and further improve economic profits.
1 code implementation • 2 Apr 2022 • Jing-Xiao Liao, Bo-Jian Hou, Hang-Cheng Dong, Hao Zhang, Xiaoge Zhang, Jinwei Sun, Shiping Zhang, Feng-Lei Fan
Encouraged by this inspiring theoretical result on heterogeneous networks, we directly integrate conventional and quadratic neurons in an autoencoder to make a new type of heterogeneous autoencoders.