Monitoring and Assessment of Acoustic Emission Signatures During Fatigue Mechanism of Api5lx70 Gas Pipeline Steel
The detection of an early fatigue phenomenain a gas pipeline is crucial to avoid catastrophic consequences. Therefore,appropriate inspection is needed to assess the fatigue phenomena in a gas pipeline system. The acoustic emission (AE) technology is expected to be suitable in this regards. This pap...
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Main Authors: | , , , , |
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Format: | Article |
Language: | English |
Published: |
Faculty Mechanical Engineering, UMP
2012
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Subjects: | |
Online Access: | http://umpir.ump.edu.my/id/eprint/2883/1/MONITORING_AND_ASSESSMENT_OF_ACOUSTIC_EMISSION_SIGNATURES.pdf http://umpir.ump.edu.my/id/eprint/2883/ http://jmes.ump.edu.my/images/Volume%202%20JUNE%202012/Yusof%20et%20al.pdf |
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Institution: | Universiti Malaysia Pahang |
Language: | English |
Summary: | The detection of an early fatigue phenomenain a gas pipeline is crucial to avoid catastrophic consequences. Therefore,appropriate inspection is needed to assess the fatigue phenomena in a gas pipeline system. The acoustic emission (AE) technology is expected to be suitable in this regards. This paper presents the monitoring and assessment of AE signatures during fatigue mechanism of gas pipeline material, API 5L X70 steel. The stress amplitude of 65, 60, 58 and 53% of ultimate tensile strength were done in order to observe the AE activity during the fatigue mechanism. The field measurements were also being done by commencing the AE signatures from the in- operation gas pipeline for comparison purpose. Based on the correlations of AE signatures and fatigue mechanism, it was found that the AE activities generated during fatigue mechanism were divided into three different stages. Analysis of the AE features and statistical parameters have shown that the kurtosis values of the time domain AE signatures from the third stage of the fatigue mechanism were different from the field measurement. This results show that application of the kurtosis was expected to be able to detect the time domain AE signatures from crack stage. |
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