Fault detection, identification and reconstruction of sensors in nuclear power plant with optimized PCA method

摘要

Principal component analysis (PCA) is applied for fault detection, identification and reconstruction of sensors in a nuclear power plant (NPP) in this paper. Various methods are combined with PCA method to optimize the model performance. During data preparing, singular points and random fluctuations in the raw data are preprocessed with different methods. During model developing, several criteria are proposed to select the modeling parameters for a PCA model. During fault detecting and identifying, a statistics-based method is applied to reduce the false alarms of T-2 and Q statistics, and abnormal behavior is analyzed in principal and residual spaces simultaneously to locate the faulty sensor. During data reconstructing, reconstruction effects are evaluated between different methods.