Online Fault Detection and Isolation in Advanced Heavy Water Reactor Using Multiscale Principal Component Analysis

Abstract

Signals from sensors placed at different locations are used for control and protection purposes in a nuclear reactor. It also requires in-service fault detection and isolation (FDI) for its safe operation. The sensor signals are generally a superimposition of low-frequency components representative of true values of the variables being monitored, occasional high-frequency periodical oscillations due to disturbances and faults, and sensor faults. Techniques like principal component analysis (PCA) can be used for FDI; however, it would be more meaningful if the FDI technique can also help for predictive maintenance of reactor internals through vibration spectra. To address these issues, a multiscale PCA, integrating wavelet transform with PCA and aiming to reduce the modeling cost by using only a fewer scales that contribute to the monitoring, has been proposed in this paper for online FDI of an advanced heavy water reactor (AHWR). A new mathematical formulation of the generalized likelihood ratio test for its use with wavelet approximation coefficients has also been proposed for better sensor-FDI outcomes. The proposed approach detects and isolates sensor faults and process faults using the signals from neutron detectors. Efficacy of the proposed technique is established on the simulated ex-core ion chamber data of AHWR considering different scenarios that involve localized frequency contents representative of process faults, slowly developing (incipient) sensor faults, and the simultaneous presence of two or more of these scenarios. Simulation results validate the effectiveness of the proposed scheme for online FDI in the reactor.