Research on Nuclear Power Electronic Sensor Drift Fault Detection and Data Reconstruction Based on the Seq2Seq-PCA Method

摘要: As critical sensing components in industrial automation and control systems, electronic sensors are prone to performance degradation and drift faults due to harsh operating environments. Among these, minor drift faults are characterized by slow variation and weak early-stage features, making them difficult to detect promptly using conventional methods. Once accumulated to a detectable level, such faults pose serious threats to system safety and operational stability. To address this issue, this paper proposes a sensor drift fault detection and data reconstruction method based on sequence-to-sequence model and principal component analysis (Seq2Seq-PCA). The method first selects auxiliary variables through Spearman correlation analysis to construct the input feature set. A Seq2Seq model with an attention mechanism is then employed for multi-step rolling prediction to capture the dynamic characteristics of the system. Principal component analysis is applied to the prediction residuals to establish a statistical monitoring model, enabling sensitive detection of minor drift faults. Upon fault detection, the multi-step prediction values of the Seq2Seq model are directly used as the reconstructed output, achieving seamless integration of fault detection and data reconstruction. Experimental results on the nuclear power plant simulator demonstrate that the proposed method achieves accurate detection and reliable reconstruction under various drift rates.