Effects of deep neural network parameters on classification of bearing faults

Automatic fault detection and classification for roller element bearings is an important issue for rotating machine condition monitoring. In this paper, we classify roller element bearings fault classes under two and three hidden layers' deep neural network framework based on sparse Autoencoder. This allows us to learn and extract features for the bearing vibration samples in an unsupervised manner using the encoder part of the Autoencoder. Then we form the deep neural network by stacking the encoders in each stage of the hidden layers together with the softmax layer. Classification performance using the full deep network and backpropagation compared, and effects of different deep neural network parameters on the classification accuracy are studied here.