Model-Based Deep Transfer Learning Method to Fault Detection and Diagnosis in Nuclear Power Plants

Abstract

Deep learning–based nuclear intelligent fault detection and diagnosis (FDD) methods have been widely developed and have achieved very competitive results with the progress of artificial intelligence technology. However, the pretrained model for diagnosis tasks is hard in achieving good performance when the reactor operation conditions are updated. On the other hand, retraining the model for a new data set will waste computing resources. This article proposes an FDD method for cross-condition and cross-facility tasks based on the optimized transferable convolutional neural network (CNN) model. First, by using the pretrained model's prior knowledge, the model's diagnosis performance to be transferred for source domain data sets is improved. Second, a model-based transfer learning strategy is adopted to freeze the feature extraction layer in a part of the training model. Third, the training data in target domain data sets are used to optimize the model layer by layer to find the optimization model with the transferred layer. Finally, the proposed comprehensive simulation platform provides source and target cross-condition and cross-facility data sets to support case studies. The designed model utilizes the strong nonlinear feature extraction performance of a deep network and applies the prior knowledge of pretrained models to improve the accuracy and timeliness of training. The results show that the proposed method is superior to achieving good generalization performance at less training epoch than the retraining benchmark deep CNN model.