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This paper presents the validity and usefulness of early warning in online monitoring system for nuclear power plants. Early warning is one of the core functions of the online monitoring system, which uses pattern recognition to predict and alert potential problems in the equipment or system. This function was developed by using the AAKR technique and has been operated since 2016. We show that the early warning system is operating properly through an analysis of the operation result of the system, and present two cases that represent the role and reliability of the system. In the first case, the system detected a failure of measuring instrument in advance. And the other case, the system went through pattern relearning to overcome environmental changes.

Calculations of fretting wear depth due to the turbulence excitation around steam generator tubes, for various wear scars, are carried out numerically. Four typical wear topologies, namely, round-, crescent-, flat-, and oblique-shaped wear scars, are adopted to represent the configuration of the wear volume. Oblique wear shows the most severe case for the wear time history, whereas both round-and crescentshaped wears have smaller increasing rates of wear histories than flat-or oblique-shaped wears. It can be estimated that a high cross flow, around the U-bend region of the steam generator tube, significantly enhances the wear phenomena because the basic wear scar, at the contact point between the tube and its support, is flat or oblique. Parametric studies on the inclined angle and radial clearance are also carried out for oblique and crescent wear shapes.