An ensemble data-driven method for fault detection and diagnosis of digital control systems in nuclear power plants

Fault detection and diagnosis (FDD) is essential for maintaining safety and preventing hazardous situations in industrial process control. Effective fault diagnosis allows for the timely detection and correction of anomalies, preventing potential disruptions and maintaining optimal performance. In the paper, we present a unified framework for fault detection and diagnosis by combining the real-time sensitivity of the moving window particle filtering (PF) with the diagnostic precision of the gene