The Use of Acoustic Emission for Leak Detection in Steel Pipe

2015 ◽  
Vol 771 ◽  
pp. 88-91
Author(s):  
I.B. Ardhana Putra ◽  
Iwan Prasetiyo ◽  
Dewi Permata Sari
Keyword(s):  
Acoustic Emission ◽  
Detection System ◽  
Propagation Constant ◽  
Leak Detection ◽  
Galvanized Steel ◽  
Steel Pipe ◽  
Experimental Result ◽  
Digital Oscilloscope ◽  
Ae Signal ◽  
Experimental Rig

A leak detection system using acoustic emission methods is developed. For this, an experimental rig to detect leak was built using 8” galvanized steel pipe. The length of the pipe is 2 meters. A leak was made with 3 mm diameter and located in 1 meter from the end pipe. The pipe was filled with water and compressed until certain pressure reached. An acoustic emission transducer from Brüel and Kjær type 8313 is mounted on the pipe wall and connected to digital oscilloscope to detect AE signal. The experiment conducted by placing a sensor at a distance of 15 cm, 30 cm, 45 cm, 60 cm, and 75 cm from the position of the leak. Measurements were also performed with the variation of the pressure 3 bar, 4 bars, 5 bars, 6 bars, and 7 bar for those points.Considering acoustic emission wave travelling on pipe is plane wave, leak detection using energy attenuation emission become possible that is different from the method commonly used. Propagation constant is thus required and obtained based on experimental result where the amplitude varies with the spatial and pressure. It is found that for the case considered here. Subsequently, distance of leak location can be determined by the propagation constant and the ratio of energy. Using this method, the error of prediction is about 15.8 %.

Development of Leak Detection System for PVC Pipeline Through Vibro-Acoustic Emission

2018 ◽  
Author(s):  
Daniel C. Ferino ◽  
Raniel P. Jose ◽  
John Revilo M. Ochoa ◽  
Vincci V. Villamiel ◽  
Jasper Meynard P. Arana
Keyword(s):  
Acoustic Emission ◽  
Detection System ◽  
Leak Detection

Pipeline Leak Detection System for Oil and Gas Flowlines

1997 ◽  
Vol 119 (1) ◽  
pp. 105-109 ◽  
Author(s):  
J. M. Rajtar ◽  
R. Muthiah
Keyword(s):  
Acoustic Emission ◽  
Oil And Gas ◽  
Production Systems ◽  
Detection System ◽  
Leak Detection ◽  
Production Costs ◽  
Closed Field ◽  
Two Phase ◽  
Expected Performance ◽  
Potential Applications

Petroleum fluids in production systems are frequently transported by surface steel pipelines of low diameter working at low pressures and under a two-phase flow regime. These pipelines operate without permanent, continuous supervision for leaks. The leaked volume is usually high before the leak is noticed and stopped. High leak volumes pollute the environment and increase production costs. This paper describes the expected performance of the acoustic emission leak detection system for low pressure flowlines in oil and gas gathering installations. The developed system detects acoustic emission signals generated by leaks. Specific features of the system are discussed. The system was tested in a closed field scale two-phase flowloop. Example results of tests are reported. The paper is completed with conclusions and discussion of potential applications of the system.

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

2017 ◽  
Vol 24 (18) ◽  
pp. 4122-4129 ◽  
Author(s):  
YJ Song ◽  
SZ Li
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Engineering Application ◽  
Leak Detection ◽  
Identification Accuracy ◽  
Galvanized Steel ◽  
Screw Thread ◽  
Ann Model ◽  
Pipe System ◽  
Steel Pipes

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.

The Identification Technology of Rolling Bearing Acoustic Emission Fault Pattern Based on Harmonic Wavelet Packet and SVM

2011 ◽  
Vol 52-54 ◽  
pp. 2039-2044
Author(s):  
Fen Lou Zhai ◽  
Li Xin Gao ◽  
Neng Chun Gong ◽  
Yong Gang Xu ◽  
Ming Shi Feng
Keyword(s):  
Acoustic Emission ◽  
Frequency Band ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Experimental Result ◽  
Support Vector ◽  
Fault Pattern ◽  
Fault Type ◽  
Harmonic Wavelet ◽  
Ae Signal

As the energy distribution in each frequency band of rolling bearing acoustic emission (AE) signal is related to its fault type, so we can use the harmonic wavelet packet to decompose the rolling bearing AE signal of different fault into different frequency band, combine energy in each frequency band together to be a feature vector of the Support Vector Machines (SVM), then being applied to identify the fault through SVM. This paper also compared the Harmonic wavelet packet and Daubechies wavelet packet as well as the SVM and neural networks. The experimental result shows that for the fault pattern identification, the method that combines harmonic wavelet packet decomposition and SVM together can be effective.

The Identification Technology of Rolling Bearing Acoustic Emission Fault Pattern Based on Redundant Lifting Wavelet Packet and SVM

2011 ◽  
Vol 52-54 ◽  
pp. 2033-2038
Author(s):  
Li Xin Gao ◽  
Fen Lou Zhai ◽  
Bang Xi Hu ◽  
Jiang Hua Zhou ◽  
Jian Hua Chen ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Frequency Band ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Experimental Result ◽  
Support Vector ◽  
Fault Pattern ◽  
Fault Type ◽  
Ae Signal ◽  
Lifting Wavelet

As the energy distribution in each frequency band of rolling bearing acoustic emission (AE) signal is related to its fault type, so we can use the redundant lifting wavelet packet to decompose the rolling bearing AE signal of different fault into different frequency band, combine energy in each frequency band together to be a feature vector of the Support Vector Machines (SVM), then being applied to identify the fault through SVM. This paper also compared the redundant lifting wavelet packet and Daubechies wavelet packet as well as the SVM and neural networks. The experimental result shows that for the fault pattern identification, the method that combines redundant lifting wavelet packet decomposition and SVM together can be effective.

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