using WGAN to generate fault bearing vibration signals
request:
python 3.5+
tensorflow-gpu
numpy scipy os
open cmd and cd to the folder
$ python train.py
--learning_rate 0.000001 #change the learning rate,default 0.000001
--epoch 2000000 #how much epochs to train,default 2000000
--sample_rate 50000 #how many epochs you want to sample once,default 50000
--train_data x1 #there 9 kinds of signals you can choose,default x1
--train_times 3 #default 3, you can try 4 or 5
your model will be saved at ./checkpoint/ per 100000 epochs
$ python test.py
test you model, output will be saved at ./output/
It takes about 11 hours to run 2000000 epochs in Titan XP.
signals are from here http://csegroups.case.edu/bearingdatacenter/home
its difficult to train this model, please try different learning rate
if you want to train with your own data, please normalize it