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

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.