• The Journal of the Acoustical Society of Korea
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• v.31 no.7
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• pp.449-460
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• 2012

The importance of the leak detection of a buried pipe in a power plant of Korea is being emphasized as the buried pipes of a power plant are more than 20 years old. The objective of this work is to enhance the capability of the leak detection technique in a noisy environment. For this purpose, a modified cross-correlation method that can effectively remove the rotating machinery noise component is suggested. In addition, a method for leak point detection using phase information of cross-spectrum is suggested. The validity of the proposed method is verified by performing an experiment. The experimental result demonstrates that the performance of the cross-correlation method can be enhanced by reducing the periodic noise components due to mechanical equipment.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.33-47
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• 2018

City gas pipelines are buried underground. Because of this, pipeline is hard to manage, and can be easily damaged. This research proposes a real time prediction system that helps experts can make decision about pressure anomalies. The gas pipline pressure data of Jungbu City Gas Company, which is one of the domestic city gas suppliers, time variables and environment variables are analysed. In this research, regression models that predicts pipeline pressure in minutes are proposed. Random forest, support vector regression (SVR), long-short term memory (LSTM) algorithms are used to build pressure prediction models. A comparison of pressure prediction models' preformances shows that the LSTM model was the best. LSTM model for Asan-si have root mean square error (RMSE) 0.011, mean absolute percentage error (MAPE) 0.494. LSTM model for Cheonan-si have RMSE 0.015, MAPE 0.668.

• Journal of the Korean Institute of Gas
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• v.18 no.5
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• pp.12-19
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• 2014

To minimize the risk of corrosion on buried pipeline and to maximize the efficiency of cathodic protection, various indirect inspection techniques have been used for decades. In the United States, 49 CFR has regulated the external corrosion direct assessment for buried pipelines. In Korea, there is no provision for external corrosion direct assessment but there is only, according to the KGS Code, provision that if the survey of the defects of buried pipeline and the leakage test for the pipe were conducted, it is deemed to leakage inspection. We, therefore, have suggested external corrosion direct assessment method appropriate to domestic status through the survey of the regulations and standards of UK and the USA and the investigation of domestic situation on coating damage detection method. The proposed external corrosion direct evaluation method was used as the basis when introducing the precision safety diagnosis regulation for the medium-pressure pipe in Korea.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1044-1053
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• 2022

Time domain reflectometry (TDR) is a diagnostic technique to evaluate the physical integrity of cable and finds application in leak detection and localization of piping system. In this study, a cable-shaped leak detection sensor was proposed using the TDR technique for monitoring leakage detection of ship's engine room seawater piping system. The cable sensor was developed using a twisted pair arrangement and wound by an absorbent material. The availability and performance of the sensor for leak detection and localization were evaluated on a lab-scale pipeline set up. The developed sensor was installed onto the pipes and flanges of the lab-scale set up and various TDR waveforms were acquired and analyzed according to the dif erent variables including the number of twists and sheath thickness. The result indicated that the twisted cable sensor was able to produce clear and smooth signal as compared to the TDR sensor with a parallel arrangement. The optimal number of twist was determined to be above 10 per the unit length. The optimal diameter of sheath thickness that results in the desired sensitivity was determined to be ranging from 80% up to 120% of the diameter of the conductor. The linear regression analysis for estimation of leak localization was carried out to estimate the location of the leakage, and the result was a determination coefficient of 0.9998, indicating a positive relationship with the actual leakage point. The proposed TDR based leak detection method appears to be an effective method for monitoring leakage of ship's seawater piping system.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.414-421
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• 2024

In this paper, we design and verify a system that can detect piping leakage noise in an environment with significant reverberation and reflection using a multi-channel acoustic sensor module as a technology to prevent major plant accidents caused by leakage. Four-channel microphones arranged in a tetrahedron are designed as a single sensor module to measure three-dimensional sound intensity vectors. In an environment with large effects of reverberation and reflection, the measurement error of each sensor module increases on average, so after placing multiple sensor modules in the field, measurement results showing locations with large errors due to effects such as reflection are excluded. Using the intersection between three-dimensional vectors obtained from several pairs of sensor modules, the coordinates where the sound source is located are estimated, and outliers (e.g., positions estimated to be outside the site, positions estimated to be far from the average position) are detected and excluded among the points. For achieving aforementioned goal, an excluding algorithm by deciding the outliers among the estimated positions was proposed. By visualizing the estimated location coordinates of the leakage sound on the site drawing within 1 second, we construct and verify a system that can detect the location of the leakage sound in real time and enable immediate response. This study is expected to contribute to improving accident response capabilities and ensuring safety in large plants.