• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Local–Global Modeling and Distributed Computing Framework for Nonlinear Plant-Wide Process Monitoring With Industrial Big Data
【24h】

Local–Global Modeling and Distributed Computing Framework for Nonlinear Plant-Wide Process Monitoring With Industrial Big Data

机译：用于工业大数据的非线性植物范围内监测的本地 - 全球建模与分布式计算框架

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

Industrial big data and complex process nonlinearity have introduced new challenges in plant-wide process monitoring. This article proposes a local-global modeling and distributed computing framework to achieve efficient fault detection and isolation for nonlinear plant-wide processes. First, a stacked autoencoder is used to extract dominant representations of each local process unit and establish the local inner monitor. Second, mutual information (MI) is used to determine the neighborhood variables of a local unit. Afterward, a joint representation learning is then performed between the local unit and the neighborhood variables to extract the outer-related representations and establish the outer-related monitor for the local unit. Finally, the outer-related representations from all process units are used to establish global monitoring systems. Given that the modeling of each unit can be performed individually, the computation process can be efficiently completed with different CPUs. The proposed modeling and monitoring method is applied to the Tennessee Eastman (TE) and laboratory-scale glycerol distillation processes to demonstrate the feasibility of the method.
机译：工业大数据和复杂的过程非线性在植物范围的过程监测中引入了新的挑战。本文提出了本地全球建模和分布式计算框架，以实现非线性植物范围内的有效故障检测和隔离。首先，堆叠的AutoEncoder用于提取每个本地过程单元的主导表示并建立本地内部监视器。其次，互信息（MI）用于确定本地单元的邻域变量。之后，然后在本地单元和邻域变量之间执行关节表示学习以提取与外部相关的表示并建立本地单元的外部相关监视器。最后，所有处理单元的外部相关表示都用于建立全球监测系统。鉴于每个单元的建模可以单独执行，可以用不同的CPU有效地完成计算过程。该拟议的建模和监测方法应用于田纳西州柴刀（TE）和实验室规模的甘油蒸馏过程，以证明该方法的可行性。

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