Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions

Abstract

The performance of data-driven nuclear power plant fault diagnosis models trained by limited data cannot be guaranteed, the distribution discrepancy between training set (source domain) and test set (target domain) caused by varying operating conditions will seriously restrict their practical applications. To generalize the diagnostic knowledge learnt from labeled source domain to unlabeled target domain, a novel transfer learning method based on Maximum Mean Discrepancy (MMD) and Convolutional Neural Network (CNN) is proposed, which can reduce the domain discrepancy of the extracted features between source domain and target domain, by appending the MMD-based distribution discrepancy to the objective function of CNN. Numerical experiments with high-dimensional and strong-nonlinear complex nuclear power plant simulation data are conducted, results show that significant improvement in the diagnostic accuracy of target domain can be achieved on most transfer tasks. Besides, the influence of adopting different kernel functions (Linear kernel, Sigmoid kernel, Laplace kernel and Gaussian kernel) to calculate MMD is also studied, better transfer effect can be achieved when Gaussian kernel is used, including higher accuracy, faster convergence rate and less negative transfer. Overall, the proposed method is promising to expand the application scope of nuclear power plant intelligent fault diagnosis to varying operating conditions.