基于改进ELMD和多尺度熵的管道泄漏信号识别

doi:10.16265/j.cnki.issn1003-3033.2019.08.017

中国安全科学学报 ›› 2019, Vol. 29 ›› Issue (8): 105-111.doi: 10.16265/j.cnki.issn1003-3033.2019.08.017

基于改进ELMD和多尺度熵的管道泄漏信号识别

郝永梅¹ 副教授, 杜璋昊¹, 杨文斌¹, 邢志祥¹ 教授, 蒋军成¹ 教授, 岳云飞² 高级工程师

1 常州大学环境与安全工程学院,江苏常州 213164;
2 江苏省特种设备安全监督检验研究院常州分院,江苏常州 213161

收稿日期:2019-04-16 修回日期:2019-06-15 发布日期:2020-10-21
作者简介:郝永梅 (1970—),女,重庆人,硕士,副教授,主要从事消防工程及油气储运风险分析研究。E-mail: hymzcs@cczu.edu.cn。
基金资助:
江苏省重点研发计划专项项目(BE2018642);江苏省研究生科研创新项目(KYCX18_2622);常州市科技支撑计划(社会发展)项目(CE20185024)。

Pipeline leakage signal identification based on improved ELMD and multi-scale entropy

HAO Yongmei¹, DU Zhanghao¹, YANG Wenbin¹, XING Zhixiang¹, JIANG Juncheng¹, YUE Yunfei²

1 School of Environmental and Safety Engineering, Changzhou University, Changzhou Jiangsu 213164, China;
2 Branch of Changzhou, Jiangsu Special Equipment Safety Supervision and Inspection Institute, Changzhou Jiangsu 213161, China

Received:2019-04-16 Revised:2019-06-15 Published:2020-10-21

摘要/Abstract

摘要： 为预防城市管道泄漏事故,准确提取管道泄漏信号的特征,首先提出一种改进的总体局域均值分解(ELMD)与多尺度熵的管道泄漏信号识别方法,通过峰值波形匹配延拓法处理端点处的信号,减弱端点处信号分量的畸变、失真;然后对管道原始泄漏信号进行ELMD分解,得到一系列乘积函数(PF),计算各PF分量的多尺度熵值,根据熵值的大小筛选出含有主要泄漏信息的PF分量,消除背景噪声的影响;最后构建反向传播(BP)神经网络,并识别泄漏信号。结果表明:该方法减少了分解后的误差,能够实现管道泄漏的检测,与未改进的ELMD方法相比,泄漏信号的识别率更高。

关键词: 城市管道, 总体局域均值分解(ELMD), 多尺度熵, 反向传播(BP)神经网络, 信号识别

Abstract: This paper is conducted with the aim of preventing urban pipeline leakage accidents and accurately extracting the characteristics of pipeline leakage signals. Firstly, an improved ELMD and multi-scale entropy pipeline leakage signal identification method was proposed. The signal at the end point was processed by using the peak waveform matching extension method so as to attenuate the distortion of signal components. Secondly, ELMD decomposition of the original leakage signal was carried out to obtain a series of product functions values, the value of whose components were calculated through multi-scale entropy. The PF component containing the main leakage information was screened according to entropy value to eliminate the impact of background noise. Finally, a BP neural network was constructed to identify leakage signals. The results show that the proposed method, reducing errors after decomposition, is able to detect pipeline leakage, and it works better in recognizing leakage signals compared with unmodified ELMD method.

Key words: urban pipeline, ensemble local mean decomposition (ELMD), multi-scale entropy, back propagation (BP) neural network, signal recognition

中图分类号:

X944.4

郝永梅, 杜璋昊, 杨文斌, 邢志祥, 蒋军成, 岳云飞. 基于改进ELMD和多尺度熵的管道泄漏信号识别[J]. 中国安全科学学报, 2019, 29(8): 105-111.

HAO Yongmei, DU Zhanghao, YANG Wenbin, XING Zhixiang, JIANG Juncheng, YUE Yunfei. Pipeline leakage signal identification based on improved ELMD and multi-scale entropy[J]. China Safety Science Journal, 2019, 29(8): 105-111.

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基于改进ELMD和多尺度熵的管道泄漏信号识别

Pipeline leakage signal identification based on improved ELMD and multi-scale entropy

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