基于小样本电气参数的抽单元故障诊断模型
Chunhua Yuan1, Zhupei Liao1, Xiangyu Li2
1School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, 110159, China.
Scientific reports
|July 2, 2025
概括
这项研究引入了一种新的方法,用于通过电气参数来诊断单元故障,克服数据限制. 该方法有效地分类故障,提高石油行业的运营可靠性.
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
- 机器学习 机器学习
背景情况:
- 装置故障诊断传统上依赖于动力计卡 (DC),这些卡容易导致传感器不稳定和损坏.
- 来自驱动电机的电气参数提供了一个更稳定和连续的数据源,用于监测单元的健康状况.
- 缺陷电气参数样本不足阻碍了智能诊断方法的应用.
研究的目的:
- 使用电气参数开发用于石油抽油机的可靠故障诊断和分类方法.
- 为解决智能诊断有限故障电参数数据的挑战.
- 为了提高单元故障检测的稳定性和有效性.
主要方法:
- 建立了一个机制模型,将现有的故障动力表卡 (DC) 转换为电参数数据.
- 通过多步预测来扩展数据集,采用了一种改进的时间序列预测神经层次插曲 (N-HiTS) 方法.
- 一个全尺度卷积神经网络 (OS-CNN) 用于最终的故障诊断和分类.
主要成果:
- 提出的方法成功地从现有的故障直流电源中生成了电气参数数据.
- 使用N-HiTS扩展数据集有效地解决了故障电气参数样本的稀缺问题.
- 在故障诊断中,OS-CNN模型实现了卓越的分类性能.
结论:
- 开发的方法为装置故障诊断提供了有效的解决方案,特别是当电气参数数据有限时.
- 这种方法提高了石油行业故障检测的可靠性和效率.
- 机制建模,N-HiTS和OS-CNN的整合为工业应用中的智能诊断提供了一个有希望的方向.
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