FEATURE EXTRACTION AND ESTABLISHMENT BASED ON PUMPING UNIT WORKING CONDITIONS AND GLOBAL FAULT IDENTIFICATION

REN Taizhu; FAN Jun; JIANG Xiaxin

doi:10.16579/j.issn.1001.9669.2025.01.002

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FEATURE EXTRACTION AND ESTABLISHMENT BASED ON PUMPING UNIT WORKING CONDITIONS AND GLOBAL FAULT IDENTIFICATION

·Vibration·Noise·Monitoring·Diagnosis· | 更新时间：2025-01-23

- FEATURE EXTRACTION AND ESTABLISHMENT BASED ON PUMPING UNIT WORKING CONDITIONS AND GLOBAL FAULT IDENTIFICATION
- Journal of Mechanical Strength Vol. 47, Issue 1, Pages: 12-19(2025)
- 作者机构：
  
  新疆大学机械工程学院，乌鲁木齐 830017
- 作者简介：
  
  FAN Jun, E-mail: xj_fanjun@163.com
- 基金信息：
  
  National Natural Science Foundation of China(11462021)
- DOI：10.16579/j.issn.1001.9669.2025.01.002
  CLC： TE933
- Published：15 January 2025，
  
  Received：03 June 2023，
  
  Revised：07 July 2023，
- 稿件说明：
移动端阅览
REN TAIZHU, FAN JUN, JIANG XIAXIN. FEATURE EXTRACTION AND ESTABLISHMENT BASED ON PUMPING UNIT WORKING CONDITIONS AND GLOBAL FAULT IDENTIFICATION. [J]. Journal of mechanical strength, 2025, 47(1): 12-19.
DOI：

REN TAIZHU, FAN JUN, JIANG XIAXIN. FEATURE EXTRACTION AND ESTABLISHMENT BASED ON PUMPING UNIT WORKING CONDITIONS AND GLOBAL FAULT IDENTIFICATION. [J]. Journal of mechanical strength, 2025, 47(1): 12-19. DOI： 10.16579/j.issn.1001.9669.2025.01.002.

摘要

针对目前抽油机井下工况故障特征分类任务难以解决，使得所建立诊断模型适应性差且识别率低的问题，通过对抽油机阀门和抽油杆运动状态的分析，首先将示功图进行数学形态学预处理；然后提出阀门开闭点获取和载荷变化特征获取的两种方法，提取到抽油机全域故障的54个全新特征，建立了抽油机工况的特征库；最后运用决策树、Logistic回归和支持向量机算法，验证了在不同工况下，该特征库均具有较好的分类效果，评估了不同故障的工况特征指标，得到各工况私有规则库。研究结果表明，提取的特征能够有效识别出抽油机全域故障，并且具有较高的识别精度。

Abstract

In response to the challenging task of fault feature classification in the current working conditions of pumping units，which results in poor adaptability and low recognition rate of the established diagnosis model. The dynamometer card was pretreated by mathematical morphology through the analysis of the motion state of the pumping unit valve and the sucker rod. Then

two methods of obtaining valve opening and closing points and load variation characteristics were proposed

and 54 new features of global faults of pumping units were extracted

and the characteristic database of working conditions of the pumping unit was established.Finally

the algorithm of decision tree

logistic regression and support vector machine was used to verify that the feature database has good classification effect under different working conditions. The characteristic indexes of different fault conditions were evaluated

and the private rule database of each working condition was obtained. The research results demonstrate that the proposed features in this study are capable of effectively identifying comprehensive faults in pumping units

exhibiting a high level of recognition accuracy.

关键词

抽油机示功图特征提取故障识别阀门

Keywords

Oil pumping machineDynamometer cardFeature extractionFault identificationValve

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