Source Detection and Separation in Power Plant Process Monitoring: Application of the Bootstrap
R. Aouada, S. Aouada, Guy d’Urso, Abdelhak M. Zoubir · 2006
We consider source enumeration and identification in the context of monitoring the cooling circuit of a pressurized-water-reactor (PWR) nuclear plant. We employ a linear instantaneous-mixture to describe the system. We use the Gerschgorin radii of the transformed covariance matrix of the data to detect the number of sources. In particular, we illustrate the advantage of employing the boot-strap in a scenario where no or little a priori knowledge is available on the statistical properties of the measured data. A specific denoising procedure is also applied to the data to alleviate the effect of small variations of the noise power over the sensors, and allow a more accurate source separation. The results show the potential of both Gerschgorin-based detection and the bootstrap in practice.