基于CNN和电气参数的抽装置的动力表卡生成
Chunhua Yuan1, Wendong Wu1, Xiangyu Li2
1School of Automation and Electrical Engineering, Shenyang Ligong University, Shengyang, 110159, Liaoning, China.
Scientific reports
|August 12, 2024
概括
这项研究引入了一种新的方法,用于使用电机电气数据和卷积神经网络 (CNN) 监控油井抽油装置. 这种方法通过提高动力表卡数据的准确性和可靠性来提高井故障诊断.
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
- 机械工程 机械工程
背景情况:
- 准确的动力表卡数据对于诊断油井故障至关重要.
- 装置上的传统负载传感器在恶劣的油田环境中容易受到损坏和错误.
- 由于环境挑战,现有的方法缺乏可靠性.
研究的目的:
- 开发一种可靠,准确的方法来获取单元数据.
- 为了克服传统的基于传感器的动力表测量的局限性.
- 提高油田生产中井故障诊断的可靠性.
主要方法:
- 开发了一个混合模型,将交流电机的电气参数与卷积神经网络 (CNN) 结合起来.
- 交流电机的数学模型被用来估计电机转速和扭矩输出.
- 美国有线电视新闻网被雇佣来弥补机械模型的不准确性.
主要成果:
- 拟议的模型与实际动力表数据显示出良好的一致性.
- 使用电气参数实现了对电机转速和扭矩的准确估计.
- 美国有线电视新闻网有效地弥补了因抽水机组机制缺陷而产生的错误.
结论:
- 电机电气参数和基于CNN的模型为获取动力表数据提供了可靠的替代方案.
- 这种方法提高了油田运营中井故障诊断的准确性.
- 人工智能与电机建模的整合在石油生产监测方面取得了重大进展.
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