• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Reliability engineering & system safety >Manifold-based Conditional Bayesian network for aging pipe yield strength estimation with non-destructive measurements
【24h】

Manifold-based Conditional Bayesian network for aging pipe yield strength estimation with non-destructive measurements

机译：Manifold-based Conditional Bayesian network for aging pipe yield strength estimation with non-destructive measurements

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

摘要

Deterioration of an aging pipeline network is a significant concern for the integrity and safety of the energy transportation infrastructure. Bulk yield strength is one of the most critical mechanical parameters for the risk assessment of aging pipes. Destructive testing for bulk yield strength is prohibitively expensive, and non -destructive testing with surface-only measurements is ideal for health management. A novel Manifold-based Conditional Bayesian network (MCBN) approach is presented to automatically classify steel type and predict yield strength for the aging pipe with non-destructive measurements. First, Uniform Manifold Approximation and Projection enhanced k-Nearest Neighbors algorithm is proposed as the first stage of MCBN to achieve dimension reduction, clustering visualization, and steel type classification. Following this, a Conditional Bayesian network (CBN) is learned and constructed in the second stage to interpret causal relationships and predict yield strength. Numerical experiments are conducted on experimental and synthetic data to verify and validate the proposed MCBN. Comparison with several existing methods is presented for the performance evaluation in steel type classification and yield strength prediction, which provides a foundation for the future reliability and risk assessment of aging pipes.

著录项

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