Anomaly Detection in Nuclear Power Plants via Symbolic Dynamic Filtering

Tools of sensor-data-driven anomaly detection facilitate condition monitoring of dynamical systems especially if the physics-based models are either inadequate or unavailable. Along this line, symbolic dynamic filtering (SDF) has been reported in literature as a real-time data-driven tool of feature extraction for pattern identification from sensor time series. However, an inherent difficulty for a data-driven tool is that the quality of detection may drastically suffer in the event of sensor degradation. This paper proposes an anomaly detection algorithm for condition monitoring of nuclear power plants, where symbolic feature extraction and the associated pattern classification are optimized by appropriate partitioning of (possibly noise-contaminated) sensor time series. In this process, the system anomaly signatures are identified by masking the sensor degradation signatures. The proposed anomaly detection methodology is validated on the International Reactor Innovative & Secure (IRIS) simulator of nuclear power plants, and its performance is evaluated by comparison with that of principal component analysis (PCA).

I. Introduction

Condition monitoring and timely detection of incipient faults are critical for operational safety and performance enhancement of nuclear power plants. There are various sources of anomalous behavior (i.e., deviation from the nominal condition) in plant operations, which could be the consequence of a fault in a single component or simultaneous faults in multiple components. Often it is difficult for the plant operator to detect the anomaly and locate the associated anomalous component(s), especially if the anomaly is small and evolve slowly. Upon occurrence of an anomalous event and subsequent pervasion of its effects, the operator could be overwhelmed by the sheer volume of information, generated simultaneously from various sources. Therefore, it would be beneficial to develop an automated condition monitoring system to assist the plant operator to detect the anomalies and isolate the anomalous components.