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There are several efforts for an advanced on-line health monitoring of a piping system by using a chemical condition monitoring, a X-ray radiography and a ultrasonic techniques. In order to increase the confidence of the understanding on the health condition of the underlying piping system, an integrated and multi-disciplinary approach has been adopted in this paper. Flow-Accelerated Corrosion (FAC) under an undesirable combination of the water chemistry, flow velocity and material composition has usually caused a pipe wall thinning. The condition monitoring tests on a piping elbow of a piping system have been conducted by using several advanced sensors such as an accelerometer, ultrasonic device, and chemical sensors.

This study was aimed at developing a new method for detecting the sodium-water reaction as a result of a water leakage into the liquid sodium boundary for the liquid metal reactor. In the case of a passive acoustic method, to develop the leak detection algorithm, several signal processing methods have been evaluated. When the acoustic emission signal has a relatively high signal-to-noise ratio for the acoustic noises the spectral estimation method could be used to detect the sodium water reaction. In the case of a low signal-to-noise ratio within -5 dB to -15 dB, the system modeling and the identification methods using an autoregressive and an adaptive algorithm could be used to detect the sodium-water reaction. Regarding the active acoustic method, the basic attenuation factors of the ultrasonic beam were evaluated in the case of normal and abnormal plant conditions.

To monitor the wear and degradation on a pipe by corrosion during a plant operation, the vibration signals were measured by an accelerometer and analyzed by several analysis techniques. From the conventional methods, it was difficult to identify the wear and degradation on the pipe. And hence, the time-frequency distribution (TFD) and the adaptive cone-kernel distribution (ACKD) devised for reducing the interfering cross-terms are applied to the acquired data. They can provide the distinguishing peak patterns between the normal and corrosion signals.