Intelligent Fault Diagnosis of Rotating Machinery Based on Deep Recurrent Neural Network

Intelligent fault diagnosis methods of rotating machinery have attracted much attention in recent years. In this paper, an intelligent deep learning based method named deep recurrent neural network (DRNN) is proposed. Firstly, frequency spectrum sequences are adopted as inputs to reduce the input size. Then DRNN is constructed by the stacks of the recurrent hidden layer to automatically extract the features from the input spectrum sequences. Finally, softmax classifier is applied for fault recognition. The proposed method is verified with the experimental data, and the results confirm that the proposed method is more effective than traditional intelligent fault diagnosis methods.