Article
Engineering, Mechanical
Zahoor Ahmad, Tuan-Khai Nguyen, Akhand Rai, Jong-Myon Kim
Summary: This paper proposes a novel technique for leak detection and localization in industrial fluid pipelines. The method utilizes acoustic emission signals and a multiscale Mann-Whitney test for leak detection, and a newly developed method called acoustic emission event tracking for leak localization. The results demonstrate that this method outperforms reference methods in terms of accuracy for leak detection and localization under variable pressure and leak conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Zhiyuan Zhang, Changhang Xu, Jing Xie, Yuan Zhang, Pengqian Liu, Zichen Liu
Summary: Due to the complicated flow state of two-phase gas-liquid flows, existing leak detection techniques are not suitable for these pipelines. To prevent accidents caused by leaks, a framework combining Mel-frequency cepstral coefficient and long short-term memory based on acoustic emission (AE) is proposed. Experiments considering various operating conditions demonstrate that the MFCC-LSTM framework achieves a recognition accuracy of 98.4%. Furthermore, the framework exhibits excellent performance in leak size identification under different flow patterns.
Article
Engineering, Multidisciplinary
Jingpin Jiao, Jiawei Zhang, Yubao Ren, Guanghai Li, Bin Wu, Cunfu He
Summary: In this paper, acoustic emission signals are analyzed using a sparse representation method to extract the main components associated with pipeline leaks. Dictionary learning is performed to estimate the main leakage components, and cross-correlation analysis is used to determine the leak location. Experimental results demonstrate that the proposed method effectively improves the signal-to-noise ratio and enhances the accuracy and reliability of pipeline leak location.
Article
Engineering, Electrical & Electronic
Thang Bui Quy, Jong-Myon Kim
Summary: This article proposes a novel technique for pipeline leak detection by combining a Kalman filter and an outlier removal technique to estimate the true state, achieving higher detection rates compared to existing methods. The proposed method can achieve real-time leak detection.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Industrial
Kyumin Na, Heonjun Yoon, Jaedong Kim, Sungjong Kim, Byeng D. Youn
Summary: This paper proposes a novel method called probabilistic energy-ratio-based localization (PERL) for boiler tube leak localization in a thermal power plant using acoustic emission sensors. The method calculates the ratio of the signal energy from the specific band energy using acoustic dissipation theory and characterizes the uncertainty of the measured root mean square (RMS) in a probabilistic manner. Case studies confirm that the proposed method enables accurate localization of a boiler tube leak position.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Multidisciplinary
Hailin Bi, Chunpeng Cheng, Kunru Fu, Xiang Fan, Pengfei Zi, Zhaoxian Liu, Xudi Wang, Guizhong Zuo
Summary: A detection scheme for small leaks in tokamak vacuum chambers was investigated and validated theoretically and experimentally. The method involves scanning the chamber wall with a multi-degree-of-freedom robot arm and conducting leak tests in a small vacuum chamber, with results compared to simulations and theoretical calculations. The scheme successfully detected significant pressure fluctuations near a simulated leak in the EAST vacuum chamber.
Article
Chemistry, Analytical
Akhand Rai, Zahoor Ahmad, Md Junayed Hasan, Jong-Myon Kim
Summary: Pipeline leakage is a challenge in various industries, and the acoustic emission (AE) technology with AEE features and KS test is proposed as an effective method to accurately detect leaks.
Article
Chemistry, Analytical
Sajjad Ahmad, Zahoor Ahmad, Cheol-Hong Kim, Jong-Myon Kim
Summary: This paper proposes a reliable technique for pipeline leak detection using acoustic emission signals. The technique extracts global and local features from acoustic images obtained through continuous wavelet transform, and applies a shallow artificial neural network to identify the pipeline leak state.
Article
Engineering, Geological
Xinglin Lei, Tomohiro Ohuchi, Manami Kitamura, Xiaying Li, Qi Li
Summary: This paper applies the template matching and location method, which has been widely used in microseismic research, to laboratory acoustic emission (AE) monitoring. The method improves the detection capability and location accuracy by using template events to match poorly located events and weak signals. Experimental data calibration shows that the proposed method can be applied to various laboratory and in situ experiments.
JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING
(2022)
Article
Engineering, Manufacturing
Kaita Ito, Masahiro Kusano, Masahiko Demura, Makoto Watanabe
Summary: A novel method for real-time monitoring of microdefects generation during selective laser melting (SLM) has been developed using battery-powered equipment capable of recording and transmitting acoustic emission (AE) waveforms. The method was proven effective in detecting microcracks and pores as the origin of defects in single-layer modeling tests. This novel AE monitoring technique showed potential for improving the SLM process.
ADDITIVE MANUFACTURING
(2021)
Article
Engineering, Electrical & Electronic
Jae Cheol Lee, You Rak Choi, Jai Wan Cho
Summary: In this study, two ultrasonic leak detectors were developed for remote pipe leak detection using a non-contact, non-destructive method. One detector utilized a parabolic reflector while the other used a conical horn guide. The detectors were tested and evaluated outdoors according to ASTM E1002-05 class II standards for equipment verification. The results showed that the ultrasonic leak detector with the parabolic reflector had an average S/N ratio of 4.97 dB, while the detector with the conical horn guide had an average leak detection S/N ratio of 1.89 dB.
SENSORS AND ACTUATORS A-PHYSICAL
(2023)
Article
Chemistry, Analytical
Niamat Ullah, Zahoor Ahmed, Jong-Myon Kim
Summary: Pipelines are important for distributing liquid and gas resources, but leaks can lead to resource waste, health risks, distribution downtime, and economic loss. This article proposes a machine learning-based platform that uses acoustic emission (AE) technology to detect pinhole-sized leaks. Statistical features extracted from the AE signal are used to train machine learning models. The proposed platform achieves an exceptional overall classification accuracy of 99% for detecting leaks and pinhole-sized leaks.
Article
Chemistry, Physical
Aiping Yu, Xianghao Li, Feng Fu, Xuandong Chen, Yan Zhang
Summary: This paper proposes a new acoustic emission (AE) detection technology for detecting horizontal defects and vertical defects in sleeves with different grout compactness. The results show that the count of acoustic emission signals decreases with the increase of grouting compactness, and the reduction rate of vertical defects is larger than that of horizontal defects. The grouting compactness index constructed by wavelet packet energy ratio is highly consistent with the size of defects and has little relationship with the distribution of grout materials. The proposed method provides a new effective way for sleeve grouting compactness detection.
Article
Acoustics
Zhaoli Yan, Xiwang Cui, Yan Gao
Summary: In this paper, an active acoustic injection method is presented to stimulate specific frequency sound waves into the pipeline for leak detection based on weak echoes from the leak location. Further analysis is conducted on the relationships between the reflection coefficient and the size of the leak hole, the pipe diameter, the wall thickness of the pipe and the sound frequency. Field test results indicate that a leak localization accuracy of 0.1 m can be achieved by the proposed method, which may be beneficial to practical leakage detection in gas pipelines.
Article
Engineering, Civil
Maryam Kammoun, Amina Kammoun, Mohamed Abid
Summary: This paper introduces a novel unsupervised RNN model for leakage detection and location. It utilizes a multivariate LSTM autoencoder and multithresholding to monitor water distribution network zones. The system determines thresholds for each measurement point to identify anomalies in hydraulic data and detect leak events. Experimental results demonstrate the effectiveness and reliability of the proposed system for different data types.
WATER RESOURCES MANAGEMENT
(2023)
Article
Engineering, Environmental
Jay N. Meegoda, Wiwat Kamolpornwijit
FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING IN CHINA
(2011)
Article
Engineering, Geological
Amin Y. Pasha, Liming Hu, Jay N. Meegoda, Taghi Ebadi
GEOTECHNICAL TESTING JOURNAL
(2013)
Article
Engineering, Geological
Shengyan Gao, Jay N. Meegoda, Liming Hu
INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS
(2013)
Article
Engineering, Geological
Shengyan Gao, Jay N. Meegoda, Liming Hu
INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS
(2012)
Article
Construction & Building Technology
Jay N. Meegoda, Shengyan Gao, Sim Liu, Nicholas C. Gephart
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING
(2013)
Article
Chemistry, Analytical
Liming Hu, Jay N. Meegoda, Jianting Du, Shengyan Gao, Xiaofeng Wu
JOURNAL OF ENVIRONMENTAL MONITORING
(2011)
Article
Engineering, Civil
Steven I. Chien, Shengyan Gao, Jay N. Meegoda
JOURNAL OF TRANSPORTATION ENGINEERING
(2013)
Article
Environmental Sciences
Amin Yousefnia Pasha, Esmail Aflaki, Liming Hu, Jay N. Meegoda
SOIL & SEDIMENT CONTAMINATION
(2013)
Article
Engineering, Chemical
Shengyan Gao, Jay N. Meegoda, Liming Hu
TRANSPORT IN POROUS MEDIA
(2013)
Article
Green & Sustainable Science & Technology
Jay N. Meegoda, Hsin-Neng Hsieh, Paul Rodriguez, Jason Jawidzik
Article
Engineering, Environmental
H. Hettiarachchi, J. P. A. Hettiaratchi, C. A. Hunte, J. N. Meegoda
JOURNAL OF HAZARDOUS TOXIC AND RADIOACTIVE WASTE
(2013)
Article
Engineering, Geological
Amin Yousefnia Pasha, Liming Hu, Jay N. Meegoda, Esmail Aflaki, Jianting Du
GEOTECHNICAL TESTING JOURNAL
(2011)
Article
Engineering, Environmental
Jay N. Meegoda, Shenyan Gao, N. M. A. Al-Joulani, Liming Hu
JOURNAL OF HAZARDOUS TOXIC AND RADIOACTIVE WASTE
(2011)
Article
Engineering, Environmental
Shengyan Gao, Jay N. Meegoda, Liming Hu
JOURNAL OF HAZARDOUS TOXIC AND RADIOACTIVE WASTE
(2011)