Use SAE to classify the fault of gearbox
Through deep learning, deep neural net- works (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear func- tions. We use the SAE(Stack Autoencoder) to achieve intelligent fault diagnosis of rotating machinery.
Our datasets come from laboratory experiments,which are not very large.
There are 102 samples in the training set.you can also use other data to train the model such as health_60_1…
The test data ends with the ‘_test’
Before you run the program,you should define several variables. ‘is_need_train’: 0:you don’t need train a new model,you just use the saved model to test your sample in order to know what kind of fault it is. 1:you need to train a new model ‘save_model’: If you choose is_need_train=1,you should choose the model name to save. ‘modelindex’:If you choose is_need_train=0,you should a model you have saved to test your new sample. You shold also define architecture ,preOption_BPNN.activation
