Development of deep reinforcement learning-based fault diagnosis method for rotating machinery in nuclear power plants

Abstract

Rotating machinery fault can cause accidents like loss of flow or turbine trip that seriously threaten the operation safety of nuclear power plants (NPPs). Artificial intelligence algorithms, like machine learning or deep learning methods, can implement fault diagnosis by sample learning with no reliance on fault mechanism or physics model of the equipment. However, the accumulated fault samples are small due to high operation safety requirements of the plant. Small sample learning is challenging and usually leads to degradation of model performance. The emerging deep reinforcement learning (DRL) algorithm can incorporate the advantages of automatic feature extraction from deep learning algorithm and interactive learning from reinforcement learning algorithm, is expected to have better learning ability and robustness. In this paper, two DRL fault diagnosis models are proposed and compared. Experiment results show that the proposed models can achieve very high diagnosis accuracy of over 99% and outperform all the baseline models (support vector machine, convolutional neural network and gated recurrent unit neural network) in all test cases in this paper.