基于改进变分模态分解的抽油井偏磨程度诊断
Diagnosis of the Degree of Wear of Oil Wells Based on Improved Variational Mode Decomposition
李翔宇 1邬亦晗 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,沈阳 110159
- 折叠
摘要
目前抽油井工况分析方法与实时智能诊断技术不完善,无法及时发现、处理偏磨问题,导致抽油杆、泵等关键部件存在严重的损坏风险.为此,提出一种基于改进变分模态分解(IVMD)的抽油井偏磨程度诊断方法,其核心思想在于,扭矩和轴向力的变化会导致抽油井的偏磨程度发生改变,从而影响电参数信号的频率和幅值.首先通过改进人工鱼群算法优化变分模态分解(VMD)的分解层数与惩罚因子,然后将油井电参数信号分解成多个局部振动模态,并对生成的各局部振动模态进行特征分析,最后采用RGB图实现对抽油井偏磨程度诊断.研究结果表明,该方法可有效判断偏磨程度.
Abstract
The current methods for analyzing the working condition of pumping wells and diagno-sing issues in real-time are not yet flawless.This results in the inability to detect and address eccen-tric wear problems in a timely manner,which places crucial components like the sucker rod and pump at a significant risk of damage.Therefore,a diagnostic method for the degree of pump wear in pumping wells based on improved variational mode decomposition(IVMD)is proposed.The central idea is that changes in torque and axial force can cause variations in the degree of pump wear,thereby affecting the frequency and amplitude of electrical parameter signals.Firstly,the number of decomposition layers and penalty factors of variational mode decomposition(VMD)is optimized by improving the artificial fish swarm algorithm.Subsequently,the electrical parameters of the oil well are decomposed into multiple local vibration modes and perform characteristic analysis on the gen-erated local vibration modes.Finally,the RGB image is employed for diagnosing the degree of ec-centric wear in the pumping unit.The research results show that this method can be used to effec-tively judge the severity of eccentric wear.
关键词
抽油井/改进人工鱼群算法/改进变分模态分解/RGB图/偏磨程度诊断Key words
oil pumping well/improved artificial fish swarm algorithm/improved variational mode decomposition/RGB image/partial wear degree analysis引用本文复制引用
基金项目
国家自然科学基金(62173073)
辽宁省教育厅高等学校基本科研项目(LJKMZ20220618)
辽宁省本科教改优质教学资源建设与共享项目(SBKJGYZ-2021-06)
出版年
2024
NETL
NSTL
万方数据