Hidden Markov Model-Based Detection and Classification of Foreign Objects in Heat-Exchanger Tubes

Abstract

In recent years, there has been significant interest in prognosis and health management of heat exchanger tubes in steam generators (SG) using eddy current (EC) non-destructive evaluation (NDE) techniques. One of the recent challenges encountered in SG tube inspection is the presence of foreign objects lodged outside the tubes. Extreme vibrations cause these loose parts to rub against the tube wall and form wears on their outer surfaces which can be dangerous in the high pressure environment. Hence, there is a strong need for reliable automated signal analysis systems for early detection of foreign objects and prevention of harmful radioactive leaks at nuclear facilities. In this paper, a hidden Markov model (HMM) based classifier is proposed which can estimate the material of the foreign object from EC inspection signal. Unknown loose part material interferes with EC analysis results and lead to errors in signal processing parameters which in turn can degrade performance and reliability of automated detection systems. The proposed algorithm implements a continuous HMM classifier by using magnitude and phase based measurements obtained from the foreign object. Results of applying the algorithm on experimental data from SG tube inspection is presented, demonstrating its benefits in increasing the robustness and performance of automated signal analysis systems in detecting loose parts.