A Hybrid Leak Localization Approach Using Acoustic Emission for Industrial Pipelines

Gao, Yangde; Piltan, Farzin; Kim, Jong‐Myon

doi:10.3390/s22103963

Cited by 15 publications

(9 citation statements)

References 21 publications

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“…Structural health monitoring (SHM) has been employed in various fields as a cost-effective maintenance strategy based on sensing systems, such as acoustic emission [ 1 , 2 ], piezoelectric [ 3 , 4 ], vibration [ 5 , 6 ], and multi-source sensors [ 7 , 8 , 9 ]. SHM data are utilized in diagnostics and prognostics.…”

Section: Introductionmentioning

confidence: 99%

Degradation Feature Extraction Method for Prognostics of an Extruder Screw Using Multi-Source Monitoring Data

Park

Lee²,

Kim³

et al. 2023

Sensors

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Laboratory-scale data on a component level are frequently used for prognostics because acquiring them is time and cost efficient. However, they do not reflect actual field conditions. As prognostics is for an in-service system, the developed prognostic methods must be validated using real operational data obtained from an actual system. Because obtaining real operational data is much more expensive than obtaining test-level data, studies employing field data are scarce. In this study, a prognostic method for screws was presented by employing multi-source real operational data obtained from a micro-extrusion system. The analysis of real operational data is more challenging than that of test-level data because the mutual effect of each component in the system is chaotically reflected in the former. This paper presents a degradation feature extraction method for interpreting complex signals for a real extrusion system based on the physical and mechanical properties of the system as well as operational data. The data were analyzed based on general physical properties and the inferred interpretation was verified using the data. The extracted feature exhibits valid degradation behavior and is used to predict the remaining useful life of the screw in a real extrusion system.

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Section: Introductionmentioning

confidence: 99%

Degradation Feature Extraction Method for Prognostics of an Extruder Screw Using Multi-Source Monitoring Data

Park

Lee²,

Kim³

et al. 2023

Sensors

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“…Acoustic emission source localization technology has been widely applied in many fields, such as detection of defects or leaks in pressure vessels, storage tanks, piping systems, steel bridge panel monitoring, structural component damage and flexible piezoelectric films. For example, in [4], acoustic emission localization technology was used to locate leak location in industrial pipelines. To determine the acoustic emission localization of rocks, Huang [5] proposed an acoustic emission source localization algorithm based on full-phase fast Fourier transform and multiple intercorrelations.…”

Section: Introductionmentioning

confidence: 99%

“…The time difference between the arrival of sound waves at each sensor can be easily obtained. In [4], the location of the acoustic emission source can be easily determined by the time difference information and spatial information between the three sensors. In order to obtain single precise solution, the three sets of time differences are usually used to determine the acoustic emission source.…”

Section: Introductionmentioning

confidence: 99%

Bayesian acoustic emission time-difference method for locating the collision point of sweeping robot shell

Chen,

Li,

2023

Meas. Sci. Technol.

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The existing source localization based on acoustic emission technology often depends on the assumption of constant wave velocity inside the material. However, this assumption is hardly satisfied in actual engineering. The uncertainty of wave velocity can easily lead to low localization accuracy of sweeping robots. To overcome these deficiencies, a complete probability multi-directional measurement method based on the Bayesian inference mechanism is proposed. In the proposed method, based on the Bayesian probabilistic model, the extracted sensor time difference is subjected to probabilistic inference using the coordinate input model to determine the posterior distribution of the source’s position and wave velocity of the given arrival time. Compared with the traditional time-difference method, the proposed method achieves excellent results and outperforms the standard time-difference method in localization accuracy and anti-interference. In addition, the proposed method can conveniently, quickly, and effectively determine the location of the colliding point without considering the source emission time and wave velocity. The research in this paper provides an effective method for solving the collision localization problem of the sweeping robot shell under the acoustic emission time and wave velocity are unknown.

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“…A few pipeline leak localization methods available are cross-correlation (CC) [44][45][46][47][48][49][50] and generalized cross-correlation (GCC) [11][12][13]. CC and GCC localize the leak based on time difference estimation.…”

Section: Introductionmentioning

confidence: 99%

Novel EMD with Optimal Mode Selector, MFCC, and 2DCNN for Leak Detection and Localization in Water Pipeline

Rajasekaran,

Kothandaraman,

Pua

2023

Applied Sciences

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Significant water loss caused by pipeline leaks emphasizes the importance of effective pipeline leak detection and localization techniques to minimize water wastage. All of the state-of-the-art approaches use deep learning (DL) for leak detection and cross-correlation for leak localization. The existing methods’ complexity is very high, as they detect and localize the leak using two different architectures. This paper aims to present an independent architecture with a single sensor for detecting and localizing leaks with enhanced performance. The proposed approach combines a novel EMD with an optimal mode selector, an MFCC, and a two-dimensional convolutional neural network (2DCNN). The suggested technique uses acousto-optic sensor data from a real-time water pipeline setup in UTAR, Malaysia. The collected data are noisy, redundant, and a one-dimensional time series. So, the data must be denoised and prepared before being fed to the 2DCNN for detection and localization. The proposed novel EMD with an optimal mode selector denoises the one-dimensional time series data and identifies the desired IMF. The desired IMF is passed to the MFCC and then to 2DCNN to detect and localize the leak. The assessment criteria employed in this study are prediction accuracy, precision, recall, F-score, and R-squared. The existing MFCC helps validate the proposed method’s leak detection-only credibility. This paper also implements EMD variants to show the novel EMD’s importance with the optimal mode selector algorithm. The reliability of the proposed novel EMD with an optimal mode selector, an MFCC, and a 2DCNN is cross-verified with cross-correlation. The findings demonstrate that the novel EMD with an optimal mode selector, an MFCC, and a 2DCNN surpasses the alternative leak detection-only methods and leak detection and localization methods. The proposed leak detection method gives 99.99% accuracy across all the metrics. The proposed leak detection and localization method’s prediction accuracy is 99.54%, precision is 98.92%, recall is 98.86%, F-score is 98.89%, and R-square is 99.09%.

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A Hybrid Leak Localization Approach Using Acoustic Emission for Industrial Pipelines

Cited by 15 publications

References 21 publications

Degradation Feature Extraction Method for Prognostics of an Extruder Screw Using Multi-Source Monitoring Data

Degradation Feature Extraction Method for Prognostics of an Extruder Screw Using Multi-Source Monitoring Data

Bayesian acoustic emission time-difference method for locating the collision point of sweeping robot shell

Novel EMD with Optimal Mode Selector, MFCC, and 2DCNN for Leak Detection and Localization in Water Pipeline

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