基于EMICA-KRR的长输管道压力监测与泄漏定位方法

A long distance pipeline pressure monitoring and leakage location method based on EMICA-KRR

为解决长输管道压力监测过程中泄漏突发,难以实时预警并精确定位的问题,提出一种基于集成改进独立分量分析(EMICA)和核岭回归(KRR)的管道泄漏故障检测与定位方法.首先,建立基于EMICA算法的故障检测模型,提取并分离压力数据中的高斯信号和非高斯信号并构造相关统计量,实现故障信号分离与主分量选择;然后,根据EMICA模型获得的故障信号,进一步构造基于KRR算法的故障诊断模型,拟合数据得出故障信号压力变化幅值,实现泄漏信号的选择与泄漏故障的定位;最后进行TE(田纳西-伊斯曼)过程的数值仿真实验以验证算法的性能.仿真结果表明:EMICA-KRR算法拥有更良好的信号分离能力,可以准确识别泄漏故障信号并精确定位管段失效位置,克服了传统方法的低效、延时等缺点.

In order to deal with the problem of sudden leakage during pressure monitoring of long-distance pipelines and being difficult to give early warning and accurate leakage location,a pipeline leakage fault detection and location model is proposed by using ensemble modified independent component analysis algorithm(EMICA)and kernel ridge regression algorithm(KRR).Firstly,a fault detection model based on EMICA algorithm is established,which extracts and separates Gaussian and non-Gaussian signals from pressure data and construct related statistics to achieve fault signal separation and principal component selection.Then,based on the fault signals obtained by the EMICA model,a fault diagnosis model by using the KRR algorithm is further constructed,and the data is fitted to obtain the amplitude of the pressure change of the fault signal,and the leakage signal selection and the location of the leakage fault are achieved.Finally,numerical simulation experiments of the TE(Tennessee-Eastman)process were performed to verify the performance of the proposed algorithm.The simulation results show that the EMICA-KRR algorithm has better signal separation capability,and can accurately identify the leakage fault signal and accurately locate the failure position of the pipe segment,which overcomes the shortcomings of the traditional methods such as inefficiency and delay.