Efficient selection of time domain features for leakage detection in pipes carrying liquid commodities

G. Glentis, K. Georgoulakis, Kostas Angelopoulos
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引用次数: 3

Abstract

In this paper, a classification approach is proposed for the leakage detection in pipes carrying liquid commodities in the pipeline network of an oil refinery. Leak detection is treated as a binary classification task. Time domain features are computed from acoustic signal measurements using accelerometers mounted on the surface of the pipes. An efficient feature selection procedure is applied, combining correlation feature analysis and feature ranking. The root mean squared power and the zero crossing rate of the signals are shown to be the most discriminative among a set of candidate time domain features, which are subsequently used by a k-th nearest neighbor classifier, allowing for successful leakage detection at an affordable computational cost. The performance of the proposed scheme is evaluated using real measurements from oil refinery pipeline systems.
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液体管道泄漏检测时域特征的有效选择
本文提出了一种用于炼油厂管网液体商品输送管道泄漏检测的分类方法。泄漏检测被视为一项二元分类任务。利用安装在管道表面的加速度计从声信号测量中计算时域特征。将相关特征分析与特征排序相结合,提出了一种高效的特征选择方法。结果表明,在一组候选时域特征中,信号的均方根功率和过零率是最具判别性的,这些特征随后被第k近邻分类器使用,从而以可承受的计算成本实现成功的泄漏检测。利用炼油厂管道系统的实际测量结果对该方案的性能进行了评价。
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