摘要
针对管道信号特征提取困难,从而影响分类精度的问题,提出了一种将信号处理和智能算法相结合的管道信号检测方法。首先,利用CEEMDAN(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)对信号分解,对分解获得的固有模态(IMFs:Intrinsic Mode Functions)使用相关系数法获取有效的模态分量并进行信号重构;其次,计算重构信号的Lempel-Ziv复杂度和裕度作为特征参数;最后,将获取的特征参数输入到海鸥优化算法(SOA:Seagull Optimization Algorithm)优化后的极限学习机(ELM:Extreme Learning Machine)进行分类,并用实验室数据进行验证。实验结果表明,与常规极限学习机(ELM)和遗传算法优化后的极限学习机GA-ELM(Extreme Learning Machine Optimized by Genetic Algorithm)相比,SOA-ELM模型能有效的识别管道信号类型,且具有较高的识别率和较快的诊断速度。
Feature extraction is a troublesome problem in the pipe signal degrading the classification accuracy.To address this problem,a pipe signal diagnosis method that combines the signal processing method with the intelligence algorithm is proposed.Firstly,CEEMDAN(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)algorithm is used to decompose the signal to obtain several IMFs(Intrinsic Mode Functions)and the correlation coefficient method is used to select the useful mode function components and recombine them.Then the Lempel-Ziv complexity and Margin of the reconstructed signal are calculated as feature vector.Finally,the feature vector are inputted into the ELM(Extreme Learning Machine)optimized by SOA(Seagull Optimization Algorithm)for classification.And validation is performed with laboratory data.Experimental results show that comparing with conventional ELM and GA-ELM(Extreme Learning Machine Optimized by Genetic Algorithm).SOA-ELM model can identify the pipe signals effectively,and has higher recognition rate and faster diagnosis speed.
作者
张勇
韦焱文
王明吉
路敬祎
邢鹏飞
周兴达
ZHANG Yong;WEI Yanwen;WANG Mingi;LU Jingyi;XING Pengfei;ZHOU Xingda(School of Physic and Electronic Engineering,Northeast Petroleum University,Daqing 163318,China;College of Artificial Intelligence Energy Research Institute,Northeast Petroleum University,Daqing 163318,China;School of Electrical and Information Engineering,Northeast Petroleum University,Daqing 163318,China)
出处
《吉林大学学报（信息科学版）》
CAS
2023年第2期193-201,共9页
Journal of Jilin University（Information Science Edition）
基金
国家自然科学基金资助项目(61873058)
教育部重点实验室开放基金资助项目(MECOF2019B02)。
关键词
自适应噪声完备集合经验模态分解
Lempel-Ziv复杂度
海鸥优化算法
极限学习机
管道信号
complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)
lempel-ziv complexity
seagull optimization algorithm
extreme learning machine
pipeline signal
