Young-Chul Choi

Displacement Measurement of Multi-point Using a Pattern Recognition from Video Signal

This paper proposes a way to measure the displacement of a multi-point by using a pattern recognition from video signal. Generally in measuring displacement, gab sensor, which is a displacement sensor, is used. However, it is difficult to measure displacement by using a common sensor in places where it is unsuitable to attach a sensor, such as high-temperature areas or radioactive places. In this kind of places, non-contact methods should be used to measure displacement and in this study, images of CCD camera were used. When multi-point is measure by using a pattern recognition, it is possible to measure displacement with a non-contact method. It is simple to install and multi-point displacement measuring device so that it is advantageous to solve problems of spatial constraints.

An impact source localization technique for a nuclear power plant by using sensors of different types.

In a nuclear power plant, a loose part monitoring system (LPMS) provides information on the location and the mass of a loosened or detached metal impacted onto the inner surface of the primary pressure boundary. Typically, accelerometers are mounted on the surface of a reactor vessel to localize the impact location caused by the impact of metallic substances on the reactor system. However, in some cases, the number of accelerometers is not sufficient to estimate the impact location precisely. In such a case, one of useful methods is to utilize other types of sensor that can measure the vibration of the reactor structure. For example, acoustic emission (AE) sensors are installed on the reactor structure to detect leakage or cracks on the primary pressure boundary. However, accelerometers and AE sensors have a different frequency range. The frequency of interest of AE sensors is higher than that of accelerometers. In this paper, we propose a method of impact source localization by using both accelerometer signals and AE signals, simultaneously. The main concept of impact location estimation is based on the arrival time difference of the impact stress wave between different sensor locations. However, it is difficult to find the arrival time difference between sensors, because the primary frequency ranges of accelerometers and AE sensors are different. To overcome the problem, we used phase delays of an envelope of impact signals. This is because the impact signals from the accelerometer and the AE sensor are similar in the whole shape (envelope). To verify the proposed method, we have performed experiments for a reactor mock-up model and a real nuclear power plant. The experimental results demonstrate that we can enhance the reliability and precision of the impact source localization. Therefore, if the proposed method is applied to a nuclear power plant, we can obtain the effect of additional installed sensors.

MULTI-POINT MEASUREMENT OF STRUCTURAL VIBRATION USING PATTERN RECOGNITION FROM CAMERA IMAGE

Modal testing requires measuring the vibration of many points, for which an accelerometer, a gab sensor and laser vibrometer are generally used. Conventional modal testing requires mounting of these sensors to all measurement points in order to acquire the signals. However, this can be disadvantageous because it requires considerable measurement time and effort when there are many measurement points. In this paper, we propose a method for modal testing using a camera image. A camera can measure the vibration of many points at the same time. However, this task requires that the measurement points be classified frame by frame. While it is possible to classify the measurement points one by one, this also requires much time. Therefore, we try to classify multiple points using pattern recognition. The feasibility of the proposed method is verified by a beam experiment. The experimental results demonstrate that we can obtain good results.

Measuring Structural Vibration from Video Signal Using Curve Fitting

Many studies for measuring vibration using image signal are suggested. These methods can measure vibration of multi-points simultaneously. However, it has the disadvantage that is very sensitive to an environment. If the measured environment is not good, image signals can be measured including much background noise. So, it is difficult to obtain accurate vibration from the measured image signals. Another problem is that camera imaging has a resolution limit. Because the resolution of the camera image is relatively much lower than that of a data acquisition system, accurate measuring vibration cannot be performed. In this paper, we proposed the enhanced technique for measuring vibration using camera signal. The key word of this paper is a curve fitting. The curve fitting can exactly detect the measurement line of interested object. So, we can measure the vibration in noisy environment. Also, it can overcome the resolution limit.

MASS ESTIMATION OF IMPACTING OBJECTS AGAINST A STRUCTURE USING AN ARTIFICIAL NEURAL NETWORK WITHOUT CONSIDERATION OF BACKGROUND NOISE

It is critically important to identify unexpected loose parts in a nuclear reactor pressure vessel, since they may collide with and cause damage to internal structures. Mass estimation can provide key information regarding the kind as well as the location of loose parts. This study proposes a mass estimation method based on an artificial neural network (ANN), which can overcome several unresolved issues involved in other conventional methods. In the ANN model, input parameters are the discrete cosine transform (DCT) coefficients of the auto-power spectrum density (APSD) of the measured impact acceleration signal. The performance of the proposed method is then evaluated through application to a large-sized plate and a 1/8-scaled mockup of a reactor pressure vessel. The results are compared with those obtained using a conventional method, the frequency ratio (FR) method. It is shown that the proposed method is capable of estimating the impact mass with 30% lower relative error than the FR method, thus improving the estimation performance.

Steam Leak Detection by Using Image Signal

Steam leakage is one of the major issues for the structural fracture of pipes of nuclear power plants. Therefore a method to inspect a large area of piping systems quickly and accurately is needed. In this paper, we proposed the method for the detecting steam leakage by using image signal processing. Our basic idea come from heat shimmer which shine with a soft light that looks as if it shakes slightly. To test the performance of this technique, experiments have been performed for simple heat source and steam generator. Results show that the proposed technique is quite powerful in the steam leak detection.

Loose-part Mass Estimation Using Time-frequency Analysis

Mass estimation was derived as functions of acceleration magnitude and primary frequency. The conventional method of mass estimation used frequency data directly in the frequency domain. The signals that can be obtained sensor contained noise as well as impact signal. Therefore, how well we can detect the frequency data in noise directly determines the quality of mass estimation. To find exact frequency data, we used time-frequency analysis. The time-frequency methods are expected to be more useful than the conventional frequency domain analyses for the mass estimation problem on a plate type structure. Also it has been concluded that the smoothed WVD can give more reliable means than the other methodologies for the mass estimation in a noisy environment.

Parameter Studies for Measuring Vibration by Using Camera

Accelerometer and laser vibrometers are widely used to measure vibration of structures like a building or piping. Recently, the research measuring vibration by using camera image is introduced. This method can measure multi-points simultaneously. Also, it is possible to measure in the long distance. When we measure the vibration using a camera, the parameter analysis is needed. Therefore, this paper took the experiment for the camera lens selection. An error by the camera images characteristic was theoretically analyzed and we verified through an experiment. And the accuracy of the method measuring the vibration displacement by using the camera images was analyzed.

Monitoring Pipe Thinning Using Time-frequency Analysis

Pipe thinning is one of the major issues for the structural fracture of pipes of nuclear power plants. Therefore a method to inspect a large area of piping systems quickly and accurately is needed. In this paper, we proposed the method for monitoring pipe thinning. Our basic idea come from that a group velocity of impact wave is different as wall thickness. If the group velocity is measured, wall thickness can be estimated. To obtain the group velocity, time -frequency analysis is used. This is because an arrival time difference can be measured easily in time-frequency domain rather than time domain. To test the performance of this technique, experiments have been performed for a plate and U type pipe. Results show that the proposed technique is quite powerful in the monitoring pipe thinning.

Thickness Measurement by Using Cepstrum Ultrasonic Signal Processing

Ultrasonic thickness measurement is a non-destructive method to measure the local thickness of a solid element, based on the time taken for an ultrasound wave to return to the surface. When an element is very thin, it is difficult to measure thickness with the conventional ultrasonic thickness method. This is because the method measures the time delay by using the peak of a pulse, and the pulses overlap. To solve this problem, we propose a method for measuring thickness by using the power cepstrum and the minimum variance cepstrum. Because the cepstrums processing can divides the ultrasound into an impulse train and transfer function, where the period of the impulse train is the traversal time, the thickness can be measured exactly. To verify the proposed method, we performed experiments with steel and, acrylic plates of variable thickness. The conventional method is not able to estimate the thickness, because of the overlapping pulses. However, the cepstrum ultrasonic signal processing that divides a pulse into an impulse and a transfer function can measure the thickness exactly.