| 马亮,安腾飞,刘文黎,李德恩.基于声信号的给水管微小泄漏检测技术研究[J].电子测量与仪器学报,2024,38(12):113-123 |
| 基于声信号的给水管微小泄漏检测技术研究 |
| Research on small leakage detection technology of thepipeline based on acoustic signals |
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| DOI： |
| 中文关键词: 管道泄漏检测 变分模态分解 霜冰优化算法 Bubble熵 极限学习机 |
| 英文关键词:pipeline leakage detection variational mode decomposition rime optimization algorithm bubble entropy extreme learning machine |
| 基金项目:“十四五”国家重点研发计划(2023YFC3805802)项目、国家自然科学基金面上项目(72171094)、国家自然科学基金重大项目(52192664)、湖北省社会科学基金法治湖北专项预立项课题预立项项目资助 |
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| Author | Institution |
| Ma Liang | School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China |
| An Tengfei | School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China |
| Liu Wenli | School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China |
| Li Deen | School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China |
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| 中文摘要: |
| 为了有效解决管道泄漏信号难以从复杂背景噪声中分离以及微小泄漏特征提取困难的问题，提出一种基于VMD和ELM的管道微小泄漏工况识别的方法。首先，利用霜冰优化算法RIME改进VMD的关键参数选取，实现VMD的自适应分解。采用VMD分解产生的各阶本征模态函数之间的互信息熵值作为RIME算法参数优化中的适应度函数值，选择最佳的VMD分解参数，建立基于RIME-VMD的管道泄漏信号去噪方法。在此基础上，计算得到的滤波信号的Bubble熵值，实现对管道微小泄漏特征提取的目的。最终，将特征输入到RIME-ELM模型中进行中，实现了4种不同管道工况的识别。实验结果表明，RIME-VMD方法在滤波效果方面表现优异，其信噪比最高，达23.922 dB，说明其滤波后的重构信号中有效信号的占比最大。同时，该方法的平均绝对误差和均方误差分别为0.187和0.056，均为最小值，表明该方法重构信号中的噪声最少。将得到的故障特征向量输入到RIME-ELM模型后，分类准确率达到了95.71%，相比将故障特征向量直接输入ELM模型提高了37.4%，验证了所提出方法的有效性。 |
| 英文摘要: |
| To address the challenge of separating pipeline leakage signals from complex background noise and the difficulty of extracting small leakage features, a denoising method that uses the RIME to improve VMD is proposed. This method calculates the Bubble entropy value of the denoised signal for feature extraction, and then identifies the small leakage condition of the pipeline using an improved ELM optimized by RIME. First, RIME is used to improve the selection of key parameters for VMD, achieving adaptive decomposition. The mutual information entropy value between the IMFs generated by VMD is used as the fitness function value in the parameter optimization of the Rime algorithm, establishing a denoising method for water pipeline leakage signals based on RIME-VMD. Experiments have shown that compared to other heuristic optimization algorithms, the RIME-VMD method has the highest SNR of 23.922 dB, indicating that the reconstructed signal filtered by this method has the highest proportion of effective signal. The RIME-VMD method also has the lowest MAE and MSE, at 0.187 and 0.056 respectively, indicating that the reconstructed signal contains the least noise. Second, a method for extracting features from pipeline micro leakage signals using Bubble entropy is proposed, and these features are input into a model with ELM parameters optimized by RIME for pipeline leakage detection. Ultimately, the classification accuracy of the RIME-ELM model reached 95.71%, which is a 37.4% improvement compared to directly inputting fault feature vectors into the ELM, verifying the effectiveness of the proposed method. |
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