海洋平台油气管道疲劳裂纹AE信号特征提取及识别研究

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Abstract

In order to apply acoustic emission (AE) technology to the monitoring of fatigue cracks on oil and gas pipelines of offshore platforms, it is necessary to solve the problems of pipeline vibration interference and effective feature extraction of fatigue crack AE signals. The key to the problem lies in the study of feature extraction and identification algorithms of AE signals for fatigue cracks in pipeline structures. Based on existing research, a fatigue crack identification method was proposed based on empirical mode decomposition (EMD) as feature extraction. The problem of pipeline vibration interference and effective feature extraction of fatigue crack AE signals were linked. The characteristic elements were optimized to eliminate invalid noise interference information, and the fatigue crack signal was identified by a probabilistic neural network (PNN). The results show that PNN combined with the fatigue crack recognition method based on EMD as feature extraction can achieve a good result, which provides an experimental and theoretical basis for acoustic emission technology to monitor fatigue cracks of oil and gas pipelines on offshore platforms, and has certain guiding significance.

Key words： acoustic emission (AE) technology oil and gas pipelines of offshore platforms empirical mode decomposition(EMD) fatigue crack probabilistic neural network(PNN)

Received: 23 December 2019 Published: 28 April 2021

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	Articles by authors
	WEI Qiang1
	2
	CUI Hongbin2
	XIE Yaoguo2
	QU Xianqiang2
	LI Xu2

Cite this article:

WEI Qiang1,2,CUI Hongbin2, et al. A study on feature extraction and recognition of fatigue crack AE signals of oil and gas pipelines in offshore platforms[J]. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(8): 70-78.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2021/V40/I8/70

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