基于EMD与VPMCD的矿浆管道泄漏检测方法

针对矿浆管道工况调整给泄漏检测带来的干扰,准确提取泄漏信号的特征量是降低泄漏误报、漏报的关键.提出了一种基于经验模态分解(EMD)与变量预测模型(VPMCD)的泄漏检测方法.将压力信号分解为若干个本征模函数(IMF)分量,得到能够准确反映工况特征的局部Hilbert能量谱,并作为特征值向量,通过VPMCD分类器建立泄漏识别模型.方法应用于泄漏检测中,实验结果表明:矿浆管道在正常运行、泄漏和工况调整状态下,识别率达到95%,并综合分析流量信号,提高了泄漏检测精度.

Leak detecting method for mineral slurry pipeline based on EMD and VPMCD

Aiming at interference to leak detection of mineral slurry pipeline caused by work condition adjustment,correctly extract characteristics of leak signal is the key to reduce the leakage of the false negatives and false positives. A leak detection method based on empirical mode decomposition(EMD)and variable predictive mode based class discriminate(VPMCD)is proposed. In this method,the pressure signals are decomposed into several intrinsic mode function(IMF)components,and take local Hilbert energy spectrum which most accurately reflect the pipeline operation conditions as feature values.Leak identification model is established by VPMCD classifier.When the method is applied to the leak detection,the experimental results show that under the conditions of normal operation,leakage and work adjustment condition,the recognition rate of the mineral slurry pipeline reaches 95%,and by comprehensively analyzing the flow signals,it also improves the precision of leak detection.