Demonstration of an improved passive acoustic fault detection method on recordings from the Phénix steam generator operating at full power

A hidden Markov model method proposed earlier for passive acoustic leak detection in sodium fast reactor systems has been improved in order to clarify how to set all free model parameters and to allow smaller amounts of training data. The method is based on training the model on known background noise only and optimizing its free model parameters by a parametric study of detection performance for synthetic noises superposed onto the same background. This means that the method is not assuming any knowledge on the noise to be detected and may be used as a general fault detection method, even if the application envisaged here is leak detection for sodium fast reactors. Using recordings of background noise as well as from argon injection tests performed at full power in the Phénix sodium fast reactor plant, it is estimated that the resulting method will detect leak-like deviations from the background noise with a detection delay of a few seconds, a false alarm rate close to 10-8 per second and at signal-to-noise ratio conditions at least corresponding to an additive signal at −10 dB. The method is one-channel, i.e. using input from one single acoustic sensor only.