This repository is my work to implement CNN for Paderborn bearing fault dataset, please cite my corresponding paper here
The dataset can be downloaded from (here) (update 11/Oct/24, the dataset hasn't been available in the link)
Helper functions for data cleaning and preprocessing are written in the helper.py module, whereas helper functions for training using Pytorch Framework are written in the train_helper.py module. To select two or three layers 1D CNN to be used, please refer to nn_model.py
The notebook Paderborn_Dataset.ipynb shows the training process and the trained model is saved in the ./Model folder.
I add the capability of data cleaning and preprocessing to select several types of sensor which is:
force, phase_current_1, phase_current_2, speed, temp_2_bearing_module, torque, vibration_1
Keep in mind that the raw data (.mat files) of the Paderborn dataset must be stored in one folder in order to extract the intended signal.
If you want to analyze more than one type of sensors simultaneously, you can set the parameter data_cat of function get_df_all become a list,
i.e.: data_cat = ['vibration_1', 'phase_current_1']
credit to (XiongMeijing) for inspiring me built the model and processed the Paderborn dataset
@article{al2022concise, title={Concise convolutional neural network model for fault detection}, author={Al Firdausi, Muhammad and Ahmad, Shafiq}, journal={Communications in Science and Technology}, volume={7}, number={1}, pages={62--72}, year={2022} }