使用粗略设置-LVQ神经网络在同一井中诊断注射生产系统故障
Yi Fang Yin1, Zunce Wang1, Minzheng Jiang1
1School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing, PR China.
PloS one
|November 27, 2023
概括
这项研究引入了一种用于棒注入和生产系统的新型故障诊断系统. 粗集 (RS) -学习向量量化 (LVQ) 模型实现了91.3%的识别率,用于识别下井设备缺陷.
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
- 机器学习用于下洞系统
背景情况:
- 杆系统对于石油开采至关重要.
- 在联合注入生产系统中诊断下孔故障是具有挑战性的.
- 现有的方法可能缺乏准确性和效率.
研究的目的:
- 开发一个强大的故障诊断模型,用于同一井内独特的杆式注入和生产系统.
- 为了提高识别下井设备缺陷的准确性和效率.
- 建立一个实用的实时故障检测系统.
主要方法:
- 为系统结构分析提出了一个反向计算模型.
- 应用了不变矩特征方法来提取功率图的特征.
- 利用粗略集 (RS) 理论来减少属性和自组织地图 (SOM) 来进行数据分离.
- 使用学习矢量量化 (LVQ) 开发了一个故障诊断子系统.
主要成果:
- RS-LVQ模型实现了91.3%的故障类型识别率.
- 证明了强大的识别能力和短的诊断时间.
- 通过与实际工作条件进行比较,验证了模型的有效性和准确性.
- 该系统显示了用于下洞故障诊断的实际应用.
结论:
- 开发的RS-LVQ故障诊断系统对于棒注入生产系统的单一故障有效.
- 该方法提供了高精度,效率和实用性.
- 未来的工作应该解决复杂的下坑场景中多个同时故障的诊断.
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