A novel acoustic emission detection module for leakage recognition in a gas pipeline valve

Li, Zhenlin; Zhang, Haifeng; Tan, Dongjie; Chen, Xin; Lei, Hongxiang

doi:10.1016/j.psep.2016.10.005

Cited by 87 publications

(21 citation statements)

References 24 publications

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“…In general, features regarding the AE signals include two main categories in the time domain and the frequency domain. The most commonly used features (Li et al., 2016) are listed in Table 1, where X=[x1,x2,…,xm] denotes an AE signal in the time domain and F=[f1,f2,…,fn] denotes amplitude in the frequency domain with Xf=[xf1,xf2,…,xfn].…”

Section: Methodsmentioning

confidence: 99%

Leak detection for galvanized steel pipes due to loosening of screw thread connections based on acoustic emission and neural networks

2017

Journal of Vibration and Control

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Galvanized steel pipes with screw thread connections are widely used in indoor gas transportation. In contrast with the failure of pipe tubes, leakage in this system is prone to occur in the screw thread connections. Aiming at this specific engineering application, a method based on acoustic emission (AE) and artificial neural networks (ANNs) is proposed to detect small gas leaks. Experiments are conducted on a specifically designed galvanized steel pipe system with the manipulated leak occurring in the screw thread connection to acquire the raw AE data. The features in the time and frequency domains are extracted and selected to establish an ANN model for leak detection. It has been validated that the developed ANN-based leak detector can achieve an identification accuracy of over 98%. It is also verified that the proposed model is effective even when the AE signals due to a small leak pass over two screw thread connections or an elbow connection.

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Section: Methodsmentioning

confidence: 99%

Leak detection for galvanized steel pipes due to loosening of screw thread connections based on acoustic emission and neural networks

2017

Journal of Vibration and Control

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“…The AE-based leak detection is therefore feasible. Consequently, many researchers have applied this mechanism to detect a leak in a gas pipeline [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. The leak detection using AE signals is extremely beneficial because it is a non-destructive technique, thus it does not affect the working system [ 5 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Leak Detection for a Gas Pipeline Using a k-NN Classifier and Hybrid AE Features

Quy

Kim

2021

Sensors

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This paper introduces a technique using a k-nearest neighbor (k-NN) classifier and hybrid features extracted from acoustic emission (AE) signals for detecting leakages in a gas pipeline. The whole algorithm is embedded in a microcontroller unit (MCU) to detect leaks in real-time. The embedded system receives signals continuously from a sensor mounted on the surface of a gas pipeline to diagnose any leak. To construct the system, AE signals are first recorded from a gas pipeline testbed under various conditions and used to synthesize the leak detection algorithm via offline signal analysis. The current work explores different features of normal/leaking states from corresponding datasets and eliminates redundant and outlier features to improve the performance and guarantee the real-time characteristic of the leak detection program. To obtain the robustness of leak detection, the paper normalizes features and adapts the trained k-NN classifier to the specific environment where the system is installed. Aside from using a classifier for categorizing normal/leaking states of a pipeline, the system monitors accumulative leaking event occurrence rate (ALEOR) in conjunction with a defined threshold to conclude the state of the pipeline. The entire proposed system is implemented on the 32F746G-DISCOVERY board, and to verify this system, numerous real AE signals stored in a hard drive are transferred to the board. The experimental results show that the proposed system executes the leak detection algorithm in a period shorter than the total input data time, thus guaranteeing the real-time characteristic. Furthermore, the system always yields high average classification accuracy (ACA) despite adding a white noise to input signal, and false alarms do not occur with a reasonable ALEOR threshold.

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“…Experimental results demonstrated that the discretization approach significantly reduced the calculated amount. 5 Li et al 6 presented the acoustic emission sensor for leakage detection based on kernel principal component analysis and support vector machine classifier, which were used for the optimal feature extraction and the recognition of the leakage level, respectively. However, the methods mentioned above could not detect hydrate plugging and pipeline leak simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Event classification for natural gas pipeline safety monitoring based on long short-term memory network and Adam algorithm

Wang

Chu

et al. 2019

Structural Health Monitoring

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Hydrate plugging and pipeline leak can impair the normal operation of natural gas pipeline and may lead to serious accidents. Since natural gas pipeline safety monitoring based on active acoustic excitation can detect and locate not only the two abnormal events but also normal components such as valves and pipeline elbows, recognition and classification of these events are of great importance to provide maintenance guidance for the pipeline operators and avoid false alarm. In this article, long short-term memory (LSTM) network is introduced and applied to classify detection signals of hydrate plugging, pipeline leak, and elbow. Adaptive moment estimation (Adam) algorithm is introduced and utilized to accelerate the long short-term memory network convergence in training. Experimental results demonstrate that the network with three layers and 64 units per cell performs the best. The cross-entropy loss in training is 0.0005, and classification accuracies are all 100% in training, validation, and testing which verify the validity of the long short-term memory network. Therefore, the method based on the long short-term memory network and adaptive moment estimation algorithm can work efficiently on pipeline events classification and has great guiding significance for safety assurance of natural gas transmission.

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A novel acoustic emission detection module for leakage recognition in a gas pipeline valve

Cited by 87 publications

References 24 publications

Leak detection for galvanized steel pipes due to loosening of screw thread connections based on acoustic emission and neural networks

Leak detection for galvanized steel pipes due to loosening of screw thread connections based on acoustic emission and neural networks

Real-Time Leak Detection for a Gas Pipeline Using a k-NN Classifier and Hybrid AE Features

Event classification for natural gas pipeline safety monitoring based on long short-term memory network and Adam algorithm

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