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使用故障概率和基于测量数据的神经网络进行管道故障评估和预测
H Noroznia1, M Gandomkar1, J Nikoukar1
1Department of Electrical Engineering, Islamic Azad University, Saveh Branch, Saveh, Iran.
Heliyon
|March 12, 2024
概括
随机电流显著影响天然气管道故障率. 本研究使用正常概率分布和神经网络来估计剩余的使用寿命,改进防腐蚀保护策略.
科学领域:
- 材料科学与工程 材料科学与工程
- 腐蚀科学 腐蚀科学
- 电气工程 电气工程
背景情况:
- 金属的化学腐蚀,特别是在石油和天然气基础设施中,带来了重大的经济挑战.
- 像天然气管道这样的水下金属结构容易腐蚀,因流浪电流而加剧.
- 阴极保护是缓解埋藏管道腐蚀的主要方法,定期监测至关重要.
研究的目的:
- 研究流浪电流对天然气管道故障率的影响.
- 估计在阴极保护下天然气管道的剩余使用寿命.
- 将失败概率结果与神经网络估计的准确性进行比较.
主要方法:
- 使用正常概率分布来分析流浪电流对故障率的影响.
- 使用神经网络来估计天然气管道的剩余使用寿命 (RUL).
- 将从神经网络中获得的RUL估计与失败概率评估进行比较.
主要成果:
- 流浪电流对用于阴极保护监控的时间序列数据具有显著的,可量化的影响.
- 神经网络提供了一种估计天然气管道剩余使用寿命的方法.
- 该研究建立了预测失效概率和RUL估计之间的比较框架.
结论:
- 准确评估流浪电流的影响对于可靠的管道完整性管理至关重要.
- 神经网络分析为预测管道寿命提供了一个有希望的方法.
- 整合故障概率和RUL估计提高了关键基础设施剩余寿命评估的准确性.
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