orgxin/myresearchworkspublic

About this repo

This repo is used to store the source codes of my reseach work. It contains two main parts. The first one is the codes that I reimplemented before. The second part is the source codes of my publications. I hope this repo will be useful to you.

my_own_publications folder

1. MRA-CNN

https://github.com/ShaneSpace/MyResearchWorksPublic/blob/main/my_own_publications/mra_cnn_final_public.py

Citation:

L. Jia, T. W. S. Chow, Y. Wang and Y. Yuan, "Multiscale Residual Attention Convolutional Neural Network for Bearing Fault Diagnosis," in IEEE Transactions on Instrumentation and Measurement, 2022, doi: https://doi.org/10.1109/TIM.2022.3196742.

2. GTFE-Net

https://github.com/ShaneSpace/MyResearchWorksPublic/blob/main/my_own_publications/GTFE_Net_final_public.py

Citation:

L. Jia, T. W. S. Chow, and Y. Yuan, "GTFE-Net: A Gramian Time Frequency Enhancement CNN for bearing fault diagnosis," in Engineering Applications of Artificial Intelligence, 2023, doi: https://doi.org/10.1016/j.engappai.2022.105794.

3. CDDG

https://github.com/ShaneSpace/DGFDBenchmark.

Citation:

L. Jia, T. W. S. Chow, and Y. Yuan, "Causal Disentanglement Domain Generalization for time-series signal fault diagnosis," in Neural Networks, 2024, doi: https://doi.org/10.1016/j.neunet.2024.106099

reimplementation folder

model 01

https://github.com/ShaneSpace/MyResearchWorksPublic/blob/main/reimplementation/model01_huangwenyi_Multi1DCNN.py

Huang W, Cheng J, Yang Y, et al. An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis[J]. Neurocomputing, 2019, 359: 77-92.

model 02

https://github.com/ShaneSpace/MyResearchWorksPublic/blob/main/reimplementation/model02_zhangwei_model.py

Zhang W, Peng G, Li C, et al. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals[J]. Sensors, 2017, 17(2): 425.

model 03

https://github.com/ShaneSpace/MyResearchWorksPublic/blob/main/reimplementation/model03_pantongyang_model.py

Pan T, Chen J, Zhou Z, et al. A novel deep learning network via multiscale inner product with locally connected feature extraction for intelligent fault detection[J]. IEEE Transactions on Industrial Informatics, 2019, 15(9): 5119-5128.

model 04

https://github.com/ShaneSpace/MyResearchWorksPublic/blob/main/reimplementation/model04_pengdandan_mmcnn.py

Peng D, Wang H, Liu Z, et al. Multibranch and multiscale CNN for fault diagnosis of wheelset bearings under strong noise and variable load condition[J]. IEEE Transactions on Industrial Informatics, 2020, 16(7): 4949-4960.

model 05

https://github.com/ShaneSpace/MyResearchWorksPublic/blob/main/reimplementation/model05_liuruonan_multiscale.py

Liu R, Wang F, Yang B, et al. Multiscale kernel based residual convolutional neural network for motor fault diagnosis under nonstationary conditions[J]. IEEE Transactions on Industrial Informatics, 2019, 16(6): 3797-3806.

model 06

https://github.com/ShaneSpace/MyResearchWorksPublic/blob/main/reimplementation/model06_wenlong_model.py

Wen L, Li X, Gao L, et al. A new convolutional neural network-based data-driven fault diagnosis method[J]. IEEE Transactions on Industrial Electronics, 2017, 65(7): 5990-5998.

model 07

https://github.com/ShaneSpace/MyResearchWorksPublic/blob/main/reimplementation/model07_jiangguoqian_multiscale.py

Jiang G, He H, Yan J, et al. Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox[J]. IEEE Transactions on Industrial Electronics, 2018, 66(4): 3196-3207.

model 08

https://github.com/ShaneSpace/MyResearchWorksPublic/blob/main/reimplementation/model08_zhaojing_stim_cnn.py

Zhao J, Yang S, Li Q, et al. A new bearing fault diagnosis method based on signal-to-image mapping and convolutional neural network[J]. Measurement, 2021, 176: 109088.

model 09

https://github.com/ShaneSpace/MyResearchWorksPublic/blob/main/reimplementation/model09_liang_cnn.py

Liang H, Zhao X. Rolling bearing fault diagnosis based on one-dimensional dilated convolution network with residual connection[J]. IEEE Access, 2021, 9: 31078-31091.

model 10

https://github.com/ShaneSpace/MyResearchWorksPublic/blob/main/reimplementation/model10_zhaominghang_RSBU.py

Zhao M, Zhong S, Fu X, et al. Deep residual shrinkage networks for fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2019, 16(7): 4681-4690.