Identifying Patterns and Anomalies in Delayed Neutron Monitor Data of Nuclear Power Plant

In nuclear fission, a delayed neutron is a neutron emitted by one of the fission products any time from a few milliseconds to a few minutes after the fission event. The counts of delayed neutrons constitute a time series sequence. The analysis of such time series can prove to be very significant for purpose of predictive maintenance in nuclear power plants. In this paper we aim to identify anomalies in neutron counts, which may be generated due to possible leaks in the nuclear reactor channel. Real world case data comprising of readings from Delayed Neutron Monitors (DNM) has been analyzed. The time sequences formed by the delayed neutrons have first been symbolically represented using Symbolic Approximation Algorithm (SAX), then anomaly detection and pattern detection algorithms have been applied on them. Keywords-Time Series; Anomaly Detection; Symbollic Approximate Algorithm; patterns; dataset; Delayed Neutron Monitor