| 基于鲸鱼优化算法的变分模态分解和改进的自适应加权融合算法的管道泄漏检测与定位方法 |
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| 引用本文： | 易康,蔡昌新,廖锐全,唐文涛.基于鲸鱼优化算法的变分模态分解和改进的自适应加权融合算法的管道泄漏检测与定位方法[J].科学技术与工程,2023,23(33):14220-14230. |
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| 作者姓名： | 易康 蔡昌新 廖锐全 唐文涛 |
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| 作者单位： | 长江大学;长江大学电子信息学院;长江大学石油工程学院 |
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| 基金项目： | 国家自然科学基金面上项目(62173049)；国家自然科学基金面上项目(61772086) |
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| 摘 要： | 针对管道泄漏检测与定位方法存在负压波传播衰减、噪声干扰大、数据融合率低等3种问题，提出了基于鲸鱼优化算法(whale optimization algorithm, WOA)的变分模态分解(variational modal decomposition, VMD)和改进的自适应加权融合算法(improved adaptive weighted fusion, IAWF)的管道泄漏检测与定位方法。该方法提出了三传感器泄漏检测与定位模型，并利用抗干扰能力强的WOA-VMD算法对原始信号进行消噪处理；然后采用小波分析求消噪信号的奇异点，进一步求出压力变送器检测到负压波信号的时间差；在此基础上，利用改进的自适应加权融合算法对多传感器数据进行融合，最终得到泄漏点的实际位置。实验结果表明：该方法可以有效地滤除噪声分量，获得更精确的融合结果，定位精度高，相对定位误差可以控制在1%以内，为管道泄漏检测与定位提供了一种新方法。
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| 关 键 词： | 管道泄漏检测 多传感器 自适应加权融合 鲸鱼优化算法 变分模态分解 |
| 收稿时间： | 2023/2/10 0:00:00 |
| 修稿时间： | 2023/11/5 0:00:00 |
Pipeline leakage detection and location method based on WOA-VMD and IAWF |
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| Yi Kang,Cai Changxin,Rui Quanliao,Tang Wentao.Pipeline leakage detection and location method based on WOA-VMD and IAWF[J].Science Technology and Engineering,2023,23(33):14220-14230. |
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| Authors: | Yi Kang Cai Changxin Rui Quanliao Tang Wentao |
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| Affiliation: | Yangtze University |
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| Abstract: | Aiming at the problems of negative pressure wave propagation attenuation, large noise interference and low data fusion rate in pipeline leak detection and location methods, a pipeline leak detection and location method based on Whale Optimization Algorithm (WOA) with Variational Modal Decomposition (VMD) and Improved Adaptive Weighted Fusion (IAWF) is proposed. The method proposes a three-sensor leakage detection and location model, and uses WOA-VMD algorithm with strong anti-interference ability to denoise the original signal. Then, wavelet analysis is used to find the singular point of the de-noising signal, and the time difference of the negative pressure wave signal detected by the pressure transmitter is further calculated. On this basis, the improved adaptive weighted fusion algorithm is used to fuse the multi-sensor data and the actual location of the leak point is finally obtained. The experimental results show that the method can effectively filter out the noise components and obtain more accurate fusion results, with high localization accuracy and relative localization error within 1%. It provides a new method for pipeline leakage detection and location. |
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| Keywords: | : pipeline leak detection multi-sensor adaptive weighted fusion Whale optimization algorithm Variational modal decomposition |
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