基于机制知识的电动潜水故障诊断模型
Faming Gong1,2, Siyuan Tong1,2, Chengze Du1,2
1Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
这项研究引入了一种新的方法来诊断电动潜水 (ESP) 的故障,通过将深度学习与专家规则相结合. 机制知识集成ESP故障诊断模型 (MK-ESPFDM) 提高了油田生产的准确性和适应性.
科学领域:
- 石油工程是石油工程中的一个.
- 石油和天然气领域的人工智能
- 机械故障诊断 机械故障诊断
背景情况:
- 电动潜水 (ESP) 对于海上油田生产至关重要,但由于其设计和操作环境而存在复杂的故障.
- 目前ESP的故障诊断模型存在诸如主观性,广泛的数据需求和适应性差等局限性,导致诊断准确度低.
- 解决这些局限性对于保持高效可靠的油田运营至关重要.
研究的目的:
- 开发ESP井的先进故障诊断模型,克服现有方法的局限性.
- 整合机械知识和操作参数,以增强故障症状推断.
- 在不同的地质环境中实现实时,准确和适应性故障诊断.
主要方法:
- 通过将ESP井的机械知识与其工作参数相结合,构建一个故障症状推断模型.
- 开发一种混合故障诊断模型,将深度学习与基于规则的专家系统相结合.
- 推断和诊断模型的顺序集成,以创建机制知识集成的ESP故障诊断模型 (MK-ESPFDM).
主要成果:
- 在MK-ESPFDM中,ESP井故障的诊断准确度显著提高.
- 拟议的模型有效地减少了在故障诊断过程中的人类主观性.
- 实现了断层诊断模型对各种断层和地质环境的增强适应性.
结论:
- MK-ESPFDM提供了一个强大的解决方案,用于实时和精确的ESP井故障诊断.
- 机械知识与深度学习和专家规则的整合代表了ESP故障诊断的重大进步.
- 这项研究为ESP井故障诊断领域提供了高级别的贡献,提高了运营可靠性.
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