基于融合的RDC-RBF的机故障诊断
Bowen Li1, S Selvakumar Raja2, Jiajun Li3
1School of Computer Science, Yangtze University, Jingzhou, Hubei Province, China.
PloS one
|September 25, 2023
概括
抽单元的新故障诊断方法使用了改进的里埃描述符和快速密度聚类辐射基函数 (RBF) 神经网络. 这种方法显著提高了准确性,并缩短了诊断时间,降低了油田的资源需求.
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:
- 在中国主要油田的抽油机故障诊断目前是低效和耗时的.
- 现有的方法通常具有高的硬件资源需求,缺乏普遍适用性.
研究的目的:
- 开发一个改进的,高效的,准确的故障诊断方法,用于抽单位.
- 减少石油生产场所故障诊断所需的时间和人力资源.
主要方法:
- 提出了使用改进的富里埃描述符 (IDF) 和快速密度聚类RBF (RDC-RBF) 神经网络的故障融合诊断方法.
- 通过指示图轮的里埃转换确定特征向量.
- 使用动态自适应子搜索 (DACS) 优化了RBF参数,以提高准确性和效率.
主要成果:
- 拟议的融合RDC-RBF故障诊断方法实现了96.3%的平均检测精度.
- 与主流模型相比,证明了高准确性和较短的诊断时间.
- 成功应用于像中国利油田这样的石油生产场所.
结论:
- 开发的故障聚变诊断方法比现有技术提供了显著的改进.
- 该方法有效地减少了对抽单元故障诊断的大量人力资源的需求.
- 这种方法提高了油田运营的运营效率和可靠性.
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