A comparative study of deep learning-based fault diagnosis methods for rotating machines in nuclear power plants

Abstract

Deep learning methods with powerful automatic feature extraction and end-to-end modeling capabilities can build fault diagnosis models based on raw data without relying on manual feature extraction procedures. In this paper, a comparative study of deep learning-based fault diagnosis methods for rotating machines in nuclear power plants is conducted. 4 deep learning models, namely, Deep Feed-forward Neural Network, Convolutional Neural Network, Gated Recurrent Unit Neural Network and Convolutional Recurrent Neural Network (CRNN), are selected. 2 publicly available experimental datasets of bearing faults are selected as modeling data. The model performance is compared under 3 cases: original sample size, sample reduction and noise addition. The results show that the CRNN model can achieve state-of-the-art accuracy and the best performance in all test cases. It has the advantages of good small sample learning capability and anti-noise robustness compared to other models in this paper.