Accident identification in nuclear power plants using hidden Markov models

Abstract

The identification of the type of accident during the early stages of an accident in a nuclear power plant is crucial for the selection of the appropriate response actions. A plant accident can be identified by its time-dependent patterns, related to the principal variables. The Hidden Markov Model (HMM) can be applied to accident identification, which is a spatial and temporal pattern-recognition problem. The HMM is created for each accident from a set of training data by the maximum-likelihood estimation method, which uses an algorithm that employs both forward and backward chaining, and a Baum–Welch re-estimation algorithm. The accident identification is decided by calculating which model has the highest probability for the given test data. The optimal path for each model at the given observation is found by the Viterbi algorithm, and the probability of the optimal path is then calculated. The system uses a left-to-right HMM, including six states and 22 input variables, to classify eight types of accidents and a normal state. The simulation results show that the proposed system identifies the accident types correctly. It is also shown that the identification is performed well for incomplete input observations caused by sensor faults or by the malfunctioning of certain equipment.