The fault diagnosis of a rolling bearing is at present very important to ensure the steadiness of rotating machinery. According to the non-stationary and non-liner characteristics of bearing vibration signals, a large number of approaches for feature extraction and fault classification have been developed. An effective unsupervised self-learning method is proposed to achieve the complicated fault diagnosis of rolling bearing in this paper, which uses stacked denoising autoencoder (SDA) to learn useful feature representations and improve fault pattern classification robustness by corrupting the input data, meanwhile employs “dropout” to prevent the overfitting of hidden units. Finally the high-level feature representations extracted are set as the inputs of softmax classifier to achieve fault classification. Experiments indicate that the deep learning method of SDA combined with dropout has an advantage in fault diagnosis of bearing, and can be applied widely in future
