Abstract
•A study of possible faults that may appear in PARR-2 is presented.•Review of fault diagnosis techniques with focus on PCA and FDA is presented.•PCA and FDA models are trained with the real sensor data collected from PARR-2.•The trained models applied to online sensor data demonstrated successful diagnosis.
In nuclear reactors, safety is of prime importance in their operation. Fault detection and isolation (FDI) methods are making their applications to improve safety, reliability and availability of nuclear reactors. Among various FDI techniques, data-driven techniques are best suited for fault diagnosis of nuclear reactors because process data is available to sensors, both in normal operation and under faulty conditions. Among data-driven techniques, principle component analysis (PCA) and Fisher discriminant analysis (FDA) have been successfully applied to many industrial processes. In this paper, PCA and FDA are applied for fault detection and fault isolation in Pakistan Research Reactor-2 (PARR-2) for known faults of control rod withdrawal and external reactivity insertion. PCA model is developed using training data set obtained during normal operation of PARR-2. It is then applied to test data set collected from the reactor during control rod withdrawal fault and external reactivity insertion fault. Likewise, FDA model is constructed for the above mentioned faults using the training data set and applied to the test data for fault isolation. The results demonstrate that PCA is successful in detection of both the faults. Additionally, FDA not only detects faults, but it is also successful in isolation/localization of the two faults in PARR-2.