基于小波包能量和CS-ELM的有杆泵抽油井故障诊断
Fault Diagnosis of Sucker Rod Pumping Wells Based on Wavelet Packet Energy and CS-ELM
严永进 1袁春华1
作者信息
- 1. 沈阳理工大学自动化与电气工程学院,辽宁 沈阳 110159
- 折叠
摘要
针对目前有杆泵抽油井故障诊断人工分析方法效率低问题,提出一种基于小波包能量和布谷鸟算法优化极限学习机(CS-ELM)的有杆泵抽油井故障诊断方法.首先对电功率数据进行小波包分解得到多个子频带,计算各频带的能量值再归一化处理后组合成特征向量;然后通过CS算法优化ELM使其得到最优的输入权值和隐含层阈值;最后利用CS-ELM模型对提取的特征向量进行有杆泵抽油井故障诊断并与SVM、BP和ELM的诊断结果进行对比.仿真结果表明:CS-ELM的有杆泵抽油井故障诊断的准确率最高,验证了该方法的有效性.
Abstract
Currently,the manual analysis method for fault diagnosis of rod-pumped oil wells is inefficient,a fault diagno-sis method of sucker rod pumping wells based on wavelet packet energy and cuckoo algorithm optimized extreme learning machine(CS-ELM)is proposed in this paper.Firstly,the electric power data is decomposed by wavelet packet to obtain multiple sub-bands,and the energy values of each band are calculated and normalized to form a feature vector.Then,the CS algorithm is used to optimize the ELM to obtain the optimal input weight and hidden layer threshold.Finally,the CS-ELM model is used to diagnose the fault of the rod pumping well with the extracted feature vector and compared with the diagnosis results of SVM,BP and ELM.
关键词
小波包能量/故障诊断/极限学习机/布谷鸟算法Key words
wavelet packet energy/fault diagnosis/extreme learning machine/cuckoo algorithm引用本文复制引用
出版年
2024
NETL
NSTL
万方数据