基于改进灰狼优化算法的管道泄漏定位研究  

Research on Pipeline Leakage Localization Based on the Improved Grey Wolf Optimization Algorithm

作　　者：刘佳音 张秀珩[1] 温丹阳 孙林 LIU Jia-yin;ZHANG Xiu-heng;WEN Dan-yang;SUN Lin(School of Mechanical Engineering,Shenyang University of Technology,Shenyang 110159,China)

机构地区：[1]沈阳理工大学机械工程学院,辽宁沈阳110159

出　　处：《压缩机技术》2024年第4期28-32,共5页Compressor Technology

摘　　要：针对往复压缩机循环水管道泄漏点定位的问题,提出了一种基于改进的灰狼优化算法的管道泄漏定位方法。该方法采集携带泄漏信息的负压波信号,并对传统灰狼优化算法进行改进,提出一种改变其控制参数的收敛因子a,使其迭代前期递减速度变慢,迭代后期递减速度变快的算法,从而使改进后灰狼算法满足全局搜索要求并加快寻优速度。利用改进后的灰狼优化算法,优化VMD信号降噪和BP神经网络算法,其定位精准度相较于原始GWO、传统改进IGWO都有所提高,证明改进后的算法更适合管道泄漏定位的研究。Aiming at the problem of locating leakage points in the circulating water pipeline of reciprocating compressors,a pipeline leakage location method based on an improved Grey Wolf Optimization Algorithm was proposed in the paper.This method collects negative pressure wave signals carrying leakage information and improves the traditional Grey Wolf Optimization Algorithm by proposing an algorithm that changes the convergence factorαof its control parameters to slow down the decrease rate in the early stage of iteration and accelerate the decrease rate in the later stage of iteration.This makes the improved Grey Wolf Algorithm meet the global search requirements and accelerate the optimization speed.The improved Grey Wolf Optimization Algorithm has optimized VMD signal noise reduction and BP neural network algorithm.The accuracy was improved compared with the original GWO and the traditional improved IGWO,proving that the improved algorithm is more suitable for the study of pipeline leakage localization.

关 键 词：压缩机管道 泄漏点定位 改进灰狼优化算法 BP神经网络 负压波信号 

分 类 号：TH457[机械工程—机械制造及自动化]