Research on robustness of five typical data-driven fault diagnosis models for nuclear power plants

Abstract

Data-driven models endowed with high flexibility and practicality are becoming the mainstream of nuclear power plant fault diagnosis. However, actual field data can be tainted by undesirable and unpredictable noise or disturbances, especially under fault conditions, which brings enormous challenges to performances of the data-driven models. Consequently, it is of great practical significance to study the robustness of data-driven models. In this study, data-driven models based on SVM, RF, XGBoost, FCNN and CNN are established respectively, and multiple test sets are generated pertinently, on which the robustness of the models is explored. Besides, a novel model training method named “Add Noisy Data” (AND) is proposed to improve the anti-noise ability of the models by adding noisy data to the training set. The results show that the robustness of the models varies greatly under different data taint modes, and the AND method can significantly improve the anti-noise robustness of RF model.