A graph signal processing-based multiple model Kalman filter (GSP-MMKF) tool for predictive analytics: An air separation unit process application
参考中译:基于图形信号处理的多模型卡尔曼滤波(GSP-MMKF)预测分析工具：空分装置过程应用


          

刊名：Journal of Advanced Manufacturing and Processing
作者：Sambit Ghosh（Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute）
Lucky E. Yerimah（Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute）
Yajun Wang（Smart Operations, Center of Excellence (COE)）
Yanan Cao（Smart Operations, Center of Excellence (COE)）
Jesus Flores-Cerrillo（Smart Operations, Center of Excellence (COE)）
B. Wayne Bequette（Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute）
刊号：810B0157/I
出版年：2022
年卷期：2022, vol.4, no.4
页码：10121-1--10121-15
总页数：15
分类号：TQ
关键词：Advanced manufacturing；Process systems engineering；Smart manufacturing
参考中译：先进制造；过程系统工程；智能制造
语种：eng
文摘：The industrial Air Separations Unit (ASU) is a complicated and tightly operated process. The use of dynamic process analytics is also a key element of safe and economic operation of these processes, with increasing focus on predictive analytics to take preemptive actions. With the availability of real-time data from hundreds of sensors, the data analysis process should also consider the topology of the data, as seen in sensor networks. In this paper, a novel tool is presented that considers the complex connectivity patterns in the sensor network and uses local adaptive disturbance estimations to predict global network-scale trends. The paper introduces the emerging field of Graph Signal Processing (GSP) and presents a rigorous derivation of the tool starting from the extraction of the sensor-network (in a graph theoretical sense) from the data. This network, which is in the form of a matrix, is then used to derive a Kalman-filter type of state-space model driven by input disturbances. Multiple disturbance models (e.g., step, ramp, periodic) are included to allow the model to have different kinds of disturbance propagation. Each graph node (representing the sensors used) dynamically adapts to the most recent detected disturbance individually. These estimated disturbances are propagated to the global network using the graph. Modifications to ensure stability are also discussed. The fidelity of the tool is tested on certain downtime events and the paper concludes by discussing the advantages of the method and planned future improvements.
参考中译：工业空分装置(ASU)是一个复杂且操作严密的过程。动态流程分析的使用也是这些流程安全和经济运行的关键要素，越来越注重预测性分析以采取先发制人的行动。随着来自数百个传感器的实时数据的可用性，数据分析过程还应该考虑数据的拓扑结构，如传感器网络中所见。本文提出了一种新的工具，该工具考虑了传感器网络中复杂的连通性模式，并使用局部自适应扰动估计来预测全球网络规模的趋势。本文介绍了新兴的图形信号处理(GSP)领域，并从从数据中提取传感器网络(在图论意义上)出发，对该工具进行了严格的推导。该网络以矩阵的形式，然后被用来推导由输入扰动驱动的卡尔曼滤波型状态空间模型。包括多种扰动模型(如阶跃、斜坡、周期)，以允许模型具有不同种类的扰动传播。每个图形节点(代表所使用的传感器)分别动态地适应最近检测到的干扰。使用该图将这些估计的扰动传播到全局网络。还讨论了为确保稳定性而进行的修改。在特定的停机事件上对该工具的保真度进行了测试，并通过讨论该方法的优点和计划中的未来改进进行了总结。



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