摘要
运用A0模态Lamb波对罐内原油乳化层进行非浸入式液位检测。为滤去杂波,用信号EEMD分解得到的IMF1分量的能量值作为特征参数来衡量罐内不同液体介质对Lamb波能量泄漏的影响程度;为避免EEMD分解时频率失真,对信号进行EMD分解,用前4阶IMF分量的过零点数作为特征参数来衡量罐内液体介质对Lamb波频率分量的影响;针对EMD分解和EEMD分解的不足,对信号进行2阶VMD分解分离出A0模态Lamb波,用IMF分量的中心频率作为特征参数来表征罐内不同介质对A0模态和非A0模态频率分量的影响。用信号的特征参数和其对应的液体介质作为样本,建立BP神经网络液位识别模型,测试结果显示对水-乳化层界面和乳化层-原油界面的识别误差分别为0.2%和13.8%,达到较好的液位检测效果。
The A0 modal Lamb wave is used to detect the non-immersed liquid level of the crude oil emulsion layer in the tank.In order to filter out the clutter,the energy value of the IMF1 component obtained by the signal EEMD decomposition is used as a characteristic parameter to measure the influence of the different liquid media in the tank on the energy leakage of the Lamb wave;in order to avoid frequency distortion during EEMD decomposition,the signal still needs to be EMD Decomposition,using the zero-crossing points of the first four-order IMF components as characteristic parameters to measure the influence of the liquid medium in the tank on the frequency components of the Lamb wave;in view of the shortcomings of the EMD decomposition and EEMD decomposition,the signal is subjected to the second-order VMD decomposition to separate the A0 mode The Lamb wave uses the center frequency of the IMF component as a characteristic parameter to characterize the influence of different media in the tank on the A0 mode and non-A0 mode frequency components.Using the characteristic parameters of the signal and its corresponding liquid medium as samples,the BP neural network liquid level recognition model was established.The test results showed that the recognition errors of the water-emulsion layer interface and the emulsion layer-crude oil interface were 0.2%and 13.8%,respectively,reaching It has a better liquid level detection effect.
作者
常俊杰
吴中权
罗文斌
徐洋
董德秀
CHANG Junjie;WU Zhongquan;LUO Wenbin;XU Yang;DONG Dexiu(Key Laboratory of Nondestructive Testing Technology,Ministry of Education,Nanchang Hangkong University,Nanchang 330063,China;Zhuhai Gree Electric Appliance Co.,Ltd.,Zhuhai 519060,China;China Aviation Development Shenyang Liming Aviation Engine Co.,Ltd.,Shenyang 110043,China)
出处
《中国测试》
CAS
北大核心
2022年第5期31-37,共7页
China Measurement & Test
基金
国家自然科学基金(11464030)。
