• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Engineering failure analysis >Corrosion defect modelling of aged pipelines with a feed-forward multi-layer neural network for leak and burst failure estimation
【24h】

Corrosion defect modelling of aged pipelines with a feed-forward multi-layer neural network for leak and burst failure estimation

机译：逆向多层神经网络腐蚀缺陷缺陷模型，用于泄漏和突发故障估计

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Determination of the future Corrosion Defect Depth (CDD) growth of the oil and gas pipelines is vital for the management of the integrity and mitigation of failures that can affect health, safety, and the environment. To this end, this work uses the historical operating parameters for establishing the time-dependent CDD growth of corroded pipelines based on machine learning. This data-driven machine learning relies on feed-forward Subspace Clustered Neural Network (SSCN) and Particle Swarm Optimization (PSO) to estimate the CDDs of a single-SSCN by treating the first Subspace Cluster (SSC) as a regression model that comprises of the hidden and bias layers and the input variables. The multi-SSCN model is linked to the single-SSCN model through individual values decoupling, transformations and modifications of the hyperspace of the deeper layers in the SSCN model. The CDDs estimated with the SSCN models are used for a Weibull distribution dependent leak and burst failure probability estimation to compute the integrity of the pipelines at discrete sections. The results obtained demonstrate the potentials of this technique for the integrity management of corroded aged pipelines.
机译：确定石油和天然气管道的未来腐蚀缺陷深度（CDD）的生长对于管理能够影响健康，安全和环境的失败的完整性和减轻的生长至关重要。为此，本作品使用历史操作参数来建立基于机器学习的腐蚀管道的时间相关的CDD生长。该数据驱动的机器学习依赖于前馈子空间集群的神经网络（SSCN）和粒子群优化（PSO）来估计单个SSCN的CDD，通过将第一子空间群集（SSC）视为包括的回归模型隐藏和偏置层和输入变量。多SSCN模型通过SSCN模型中更深层的较深层的超微空间的分离，转换和修改链接到单个SSCN模型。使用SSCN模型估计的CDD用于Weibull分布依赖性泄漏和突发故障概率估计，以计算离散部分的管道的完整性。所获得的结果证明了这种技术对腐蚀老化的黄色管道的完整性管理的潜力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号