基于PSO-FCM的长输管道泄漏检测方法  

Novel Detection for Long-Distance Pipeline Leakage Based on PSO-FCM

作　　者：张勇[1] 王臣[1] 王闯 姜鑫蕾 刘洁[1] ZHANG Yong;WANG Chen;WANG Chuang;JIANG Xinlei;LIU Jie(School of Physics and Electronic Engineering,Northeast Petroleum University,Daqing 163318,China;School of Electronic Engineering&Information,Northeast Petroleum University,Daqing 163318,China)

机构地区：[1]东北石油大学物理与电子工程学院,黑龙江大庆163318 [2]东北石油大学电气信息工程学院,黑龙江大庆163318

出　　处：《吉林大学学报（信息科学版）》2021年第2期185-191,共7页Journal of Jilin University（Information Science Edition）

基　　金：国家自然科学基金资助项目(61873058);黑龙江省自然科学基金重点资助项目(ZD2019F001)。

摘　　要：为了提高长输管道泄漏检测的准确率,将改进模糊C均值算法应用于长输管道泄漏检测研究。在传统模糊C均值算法的基础上引入粒子群算法,对其寻找聚类中心的迭代过程进行优化,用粒子群算法替代模糊C均值的梯度下降法,以提高模糊C均值算法的聚类效率和准确率。然后分别用所得的基于粒子群优化的模糊C均值聚类模型、传统模糊C均值聚类模型以及3层BP(Back Propagation)神经网络分类模型对同一组管道泄漏检测实验数据进行处理。对比实验结果证明,基于粒子群优化的模糊C均值算法其性能优于传统的模糊C均值算法和3层BP神经网络,将其模型应用于长输管道泄漏检测的方案可行。In order to improve the accuracy and efficiency of leakage detection for long-distance pipeline,the modified fuzzy C-means algorithm is applied. Particle swarm optimization algorithm is introduced to optimize the troditional fuzzy C-means algorithm,which is used to represent the gradient descent so as to improve the efficiency and accuracy of fuzzy C-means algorithm. Then the proposed fuzzy C-means algorithm is used to analyze the same group of pipeline leakage experimental data compared with troditional fuzzy C-means algorithm and 3-layer BP( Back Propagation) neural network. The result proves that the proposed fuzzy C-means algorithm has a better property than the other two algorithms,so it is feasible to apply the PSO-based Fuzzy C-Means model in pipeline leakage detection.

关 键 词：粒子群算法 模糊C均值 长输管道泄漏检测 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]