基于多特征熵融合和局部线性嵌入的管道信号特征提取方法

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS
Dan-Ni Yang, Jingyi Lu, Hongli Dong, Zhongrui Hu
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引用次数: 7

摘要

本文研究了不同工况下油气管道声学信号的有效特征提取问题。提出了一种基于多特征熵融合和局部线性嵌入的管道泄漏检测特征提取方法。首先,通过实验从流水线信号中提取出七种更能反映信号特征的常用熵,包括排列熵、包络熵、近似熵、模糊熵、能量熵、样本熵和分散熵。通过特征融合得到七维特征向量。其次,采用LLE算法对特征向量进行降维处理,完成二次特征提取。最后,利用支持向量机(SVM)对管道的工作状态进行识别。实验结果表明,与其他降维方法、单特征熵方法和多特征熵融合方法相比,该方法能够有效地识别管道工况类型,减少管道泄漏检测中的假阴性和假阳性问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pipeline signal feature extraction method based on multi-feature entropy fusion and local linear embedding
This paper considers the problem of effective feature extraction of acoustic signals from oil and gas pipelines under different working conditions. A feature extraction of pipeline leakage detection method is proposed based on multi-feature entropy fusion and local linear embedding (LLE). First, seven kinds of commonly used entropy which can reflect the characteristics of the signal better are extracted from the pipeline signal through experiments, including permutation entropy, envelope entropy, approximate entropy, fuzzy entropy, energy entropy, sample entropy and dispersion entropy. The seven-dimensional feature vectors are obtained by feature fusion. Second, the LLE algorithm is used to reduce the dimension of the feature vector to complete the secondary feature extraction. Finally, the support vector machine (SVM) is used to identify the working conditions of the pipeline. The experimental results show that, compared with other dimensionality reduction methods, single-feature entropy method and multi-feature entropy fusion method, the proposed method can identify the types of pipeline working conditions effectively and reduce the problems of false negatives and false positives in pipeline leakage detection.
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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