A comparative study of deep learning-based fault diagnosis methods for rotating machines in nuclear power plants. (1st December 2022)

• Record Type: Journal Article
• Authors: Qian, Gensheng; Liu, Jingquan
• Is Part Of: Annals of nuclear energy. Volume 178(2022)
• Publication Date: 2022-12-01
• Subjects: Deep learning -- Fault diagnosis -- Rotating machine -- Nuclear power plant -- Comparative study; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2022.109334
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: Fault diagnosis of rotating machines is important for nuclear power plants. Four deep learning models are presented for comparative study. Three cases, original sample size, sample reduction, noise addition, are considered. The convolutional recurrent neural network achieves the best performance. 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.