This is a pytorch implementation of the paper Intelligent Bearing Fault Diagnosis Based on Open Set Convolutional Neural Network

Environment

tensorflow 1.15.0
keras      2.3.1
numpy      1.19.5
python     3.6
scikit-learn  
libmr
pillow

Data Preparation

CRWU dataset 链接：https://pan.baidu.com/s/1L9NldqxIvYtSRm_Vb9RNdg 提取码:c52u

JNU dataset 链接:https://pan.baidu.com/s/1T1Eca5S0lnlzPw9HzCdcfw 提取码:421f

SEU dataset 链接:https://pan.baidu.com/s/1Z6LYSB0QomZrTkkDfFV0ng 提取码:455m

PHM09 dataset 链接:https://pan.baidu.com/s/1lPFHe3FXHzyjWEr8V9lOWQ 提取码:32xc

Network Structure

How to run the existing code

Step 1: The training data set generation model passes train.py.

Step 2: Load the trained model passes main.py.

Step 3: Create a Weibull model for each known class.

Step 4: Distance modeling between test data and each known class Weibull model.

Step 5: Calculate the CDF probability corresponding to each test data.

Step 6: According to the CDF probability revise activation vectors, each sample is classified.

Note

For the Python interface to work, this requires preinstall Cython on the machine.

Refer to the main.py for detail implementation