Article
Chemistry, Analytical
Yanbin Mo, Lvqing Bi
Summary: In this paper, a novel time reversal-based localization method for pipeline leakage is proposed. The method improves the leak localization resolution through TR self-adaptive cancellation. Experimental results show that the proposed approach accurately reveals the leak positions and has a resolution ten times higher than the conventional method.
Article
Acoustics
Shiqiang Qin, Jian Tang, Jiacheng Feng, Yunlai Zhou, Fei Yang, Magd Abdel Wahab
Summary: This study proposes an improved empirical wavelet transform (IEWT) to overcome the inaccurate frequency band division caused by high noise and modulation edge band, and applies it to operational modal analysis (OMA) in civil structures. The IEWT segments the frequency band by determining the spectral trend using the high-frequency components removed from the Fourier spectrum. The IEWT, combined with the random decrement technique and Hilbert transform, accurately identifies the modal parameters from multi-setup ambient vibration measurements, as verified by a full-scale cable-stayed footbridge.
JOURNAL OF VIBRATION AND CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Jing Huang, Zhifen Zhang, Rui Qin, Yanlong Yu, Guangrui Wen, Wei Cheng, Xuefeng Chen
Summary: Wall penetration and weld crack leakage of pressure pipelines pose a threat to their service safety. To address the challenge of high sampling frequency in acoustic emission (AE) technology for online monitoring, a lightweight intelligent architecture is proposed to monitor crack leakage in pipeline welds. This method effectively learns high-level abstract features from compressed AE data. Through well-designed experiments with different crack leaks, the proposed method's effectiveness is validated. Results show that the number of sampling points is reduced by 80% and the compressed AE data has better characterization capabilities. Compared to other state-of-the-art methods, the proposed method outperforms in four performance metrics, offering a new possibility for pipeline leak monitoring based on AE in the industrial field.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(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, 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, Multidisciplinary
Sun Yu, Liu Wei, Jiang Chunlei, Cong Zhicheng, Wang Yingying, Cui Kunyu, Ren Anning, Yan Wendi
Summary: This study introduces a new microfiber sensor based on the Mach-Zehnder structure, which is designed specifically for detecting small gas pipeline leaks. The sensor, encased in a double layer of poly-dimethylsiloxane (PDMS) and utilizing a nut with a hole as an external fixation device, can accurately measure low-frequency acoustic pressure changes. Experimental results show that the sensor is capable of detecting acoustic signals generated by small leakage apertures measuring 0.1 mm in diameter, with a strong linear relationship between the received voltage signal and the size of the leakage aperture.
Article
Engineering, Electrical & Electronic
Masoud Mohammadgholiha, Antonio Palermo, Nicola Testoni, Jochen Moll, Luca De Marchi
Summary: This paper presents a novel implementation of Frequency Steerable Acoustic Transducers (FSATs) for guided waves (GWs) inspections. The proposed Piezoceramic FSATs exploit the inherent directional properties of ultrasonic guided waves, leading to significant hardware simplification and cost reductions. Experimental validations using a Scanning Laser Doppler Vibrometer (SLDV) show improved generation of directional GWs in a host structure.
IEEE SENSORS JOURNAL
(2022)
Article
Materials Science, Characterization & Testing
Parikshit Roy, Aloke Kumar Datta, Pijush Topdar
Summary: Health monitoring of structures is an important research area. Different techniques, including the promising acoustic emission (AE) technique, are being developed for real-time damage detection. However, AE technique has limitations when it comes to complex structure geometries. This study explores the modal AE (MAE) technique through experimental investigation to effectively monitor structures with different geometries, such as plates and trusses, and proposes a methodology to overcome the limitations of geometry and boundaries.
NONDESTRUCTIVE TESTING AND EVALUATION
(2023)
Article
Engineering, Civil
Junshi Li, Feng Wen, Yong Pan, Jun Chen, Caiqian Yang
Summary: To address the shortage of damage identification methods for modal bridge expansion joints (MBEJs), two damage indexes, DI(IPH) and DI(HS), were constructed using the instantaneous phase and Hilbert spectrum of Hilbert Huang transform. The experimental results show that this method can effectively identify the damage in different parts of MBEJs with high accuracy.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2023)
Article
Materials Science, Textiles
Yanan Yi, Binjie Xin, Yuansheng Zheng, Meiwu Shi, LanTian Lin, Cong Gao, Xueyu Zhang, Zumin Yang, Handian Li
Summary: This study characterized the textile fracture mechanism using an acoustic emission-based method, collecting signals with acoustic emission signal acquisition devices and processing them to find that orthogonal friction between warp and weft yarns has a significant impact on the acoustic emission signals.
JOURNAL OF THE TEXTILE INSTITUTE
(2021)
Article
Engineering, Multidisciplinary
Ning Zhao, Chaofan Li, Huijun Jia, Fan Wang, Zhiyue Zhao, Lide Fang, Xiaoting Li
Summary: The study utilized acoustic emission technique to quantitatively measure and analyze the flow noise of gas-liquid two-phase flow, successfully separating and identifying signals through Hilbert Huang transform and R/S analysis, and predicting energy changes using ARIMA model.
Article
Engineering, Electrical & Electronic
Wei Li, Zhiren Wang, Xiaokang Yin, Xin'an Yuan, Huimin Yang, Xinyu Shao
Summary: This article proposes a novel detection method for quantitative detection of pipeline cracks, using three circumferentially uniformly distributed probes. The signals received by two receiving probes constitute a couple of differential signals, and the differential signal enhancement algorithm can separate the crack signal from the overlapped signal, greatly improving the accuracy of crack location. The characteristic coefficient method is introduced to improve the quantitative accuracy of the crack sizes. The localization and quantification methods are verified through experiments.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Mechanical
Thang Bui Quy, Jong-Myon Kim
Summary: This paper presents a novel approach for crack detection and localization in high-pressure fluid pipelines using acoustic emission signals, which involves scanning peaks, filtering noise, localizing emission sources through time difference of arrival technique, and eliminating false emission sources by considering wave energy attenuation characteristics. By observing the distribution of emission sources according to position and time, the method can indicate the location of irregular structural changes based on emission source distribution and density along the pipeline.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Energy & Fuels
Lizhong Yao, Yu Zhang, Tiantian He, Haijun Luo
Summary: In this study, a natural gas pipeline leakage detection model based on acoustic signal is proposed, which integrates acoustic feature processing techniques and feature reconstruction to collaboratively solve the problems of background noise coverage, lack of effective features, and low fault identification accuracy. The proposed method achieves a fault identification accuracy of 95.17% on the GPLA-12 dataset, demonstrating optimal performance and broad application prospects.
Article
Engineering, Multidisciplinary
Seyed Amir Hoseini Sabzevari, Seyed Morteza Javadpour
Summary: A new approach based on low sampling rate sensors is proposed to estimate artificial leakage location in pressurized gas-filled pipelines. The proposed technique requires low sampling rate data and uses acoustic sensors localized on one side of the leakage. This paper introduces a novel technique to localize artificial leakage by implementing two simple electret microphones and attenuation analysis. Experimental results demonstrate the effectiveness of the proposed technique in low sampling rate data.
INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING
(2023)