A deep transfer learning method for system-level fault diagnosis of nuclear power plants under different power levels

Abstract

The system-level fault diagnosis technology of nuclear power plants (NPPs) can provide operating status information, and further assist operators in making decisions to support the safe operation of NPPs. In traditional intelligent fault diagnosis research, the required assumption that the training set and test set follow the same distribution limits the application of the model. The fault diagnosis methodology integrating deep learning and transfer learning is proposed in this paper to solve the problem of poor generalization performance caused by inconsistent probability distribution under different power levels. First, the monitoring parameter preprocessing is carried out to suit the input of deep convolutional neural network (DCNN), which can effectively extract the transferable features between source and target power levels. More importantly, a promising transfer learning approach is introduced to sufficiently reduce the feature distribution discrepancy between different power levels, and eventually realize the fault diagnosis of the target power level. Finally, the simulation data test verifies the effectiveness of the transfer learning method for fault diagnosis under different power levels. Based on the feature visualization technology, the data discrepancy reduction effect is intuitively present, consequently demonstrating the potential application value in improving the generalization ability of NPP fault diagnosis.