论文标题
物联网数字双胞胎的结构和格兰杰因果关系
Structural & Granger CAUSALITY for IoT Digital Twin
论文作者
论文摘要
在有关物联网中因果分析应用的基础说明性文章中，我们建立了从测量的多通道传感器数据（矢量时间）中估算结构和granger因果关系因子的基本理论和算法。矢量时间工程被建模为结构矢量自回归（SVAR）模型；估计利用Kalman滤波器和独立组件分析（ICA）方法，结构和广义的Granger因果关系因素。估计的因果因素作为围栏图表示，我们称之为因果数字双胞胎。在NASA预后数据存储库数据收集中，证明了因果数字双胞胎的实际应用。指示因果数字双胞胎用于反事实实验。 Causal Digital Twin是一种水平解决方案，适用于多个行业（例如工业，制造，汽车，消费者，建筑和智能城市）的不同用例。
In this foundational expository article on the application of Causality Analysis in IoT, we establish the basic theory and algorithms for estimating Structural and Granger causality factors from measured multichannel sensor data (vector timeseries). Vector timeseries is modeled as a Structural Vector Autoregressive (SVAR) model; utilizing Kalman Filter and Independent Component Analysis (ICA) methods, Structural and generalized Granger causality factors are estimated. The estimated causal factors are presented as a Fence graph which we call Causal Digital Twin. Practical applications of Causal Digital Twin are demonstrated on NASA Prognostic Data Repository Bearing data collection. Use of Causal Digital Twin for counterfactual experiments are indicated. Causal Digital Twin is a horizontal solution that applies to diverse use cases in multiple industries such as Industrial, Manufacturing, Automotive, Consumer, Building and Smart City.
