石油井的故障诊断方法 基于多维集群性能评估的电力图
Xingyu Liu1, Xin Meng1, Ze Hu1
1School of Electrical Information, Southwest Petroleum University, Chengdu 610500, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
本研究引入了用于实时油田监测的新型发动机功率分析,将故障检测精度提高到98.4%,并与传统动力表卡相比降低成本.
科学领域:
- 石油工程是石油工程中的一个.
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 传统的动力表卡用于油田开采中下孔监测,在实时性能和成本方面存在局限性.
- 现代开采需要更高效,更准确的条件监测解决方案.
研究的目的:
- 开发一种新的故障检测方法,用于使用电机功率参数的光束送单元.
- 增强实时监控能力,降低油田运营中的维护成本.
主要方法:
- 动态抽机组的数学建模,以将指示图转换为电力图.
- 从时间,频率和时间频率领域的电气参数中提取14个新特征.
- 开发一个新的有效性评估的模糊C-Means (FCM) 集群集成模糊的成员和几何结构.
主要成果:
- 分析显示了五种典型运行条件的独特电能图特征.
- 提出的方法实现了98.4%的故障诊断准确度,超过了SVM和ELM.
- 新的FCM评估功能准确地确定了最佳集群数量,并改善了UCI数据集上的集群性能.
结论:
- 拟议的发动机功率分析方法在实时性能,诊断准确性和油田开采的成本效益方面具有显著的优势.
- 这种方法有效地解决了传统监控方法的局限性,提高了故障诊断能力.
- 该方法可以有效地实时监控束单元井.
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