基于过程挖掘和报警数据的植物拓扑结构提取框架
A Framework for Plant Topology Extraction Using Process Mining and Alarm Data
摘要：
工厂容易出故障。为了通知操作员发生故障，警报器被用作现代计算机控制设备的基本部分。然而，由于工厂的不同部分相互连接，单个故障通常会在工厂中传播并触发（有时是大量）警报。图形化的工厂拓扑可以帮助操作员、工艺工程师和维护专家找到工厂故障的根本原因或故障的传播路径。提出了一种从报警数据中提取设备拓扑结构的方法。该方法基于流程挖掘，即基于记录的事件对流程（不一定是工程流程）进行建模的概念和算法的集合。报警数据的基于事件的性质以及记录的报警的时间顺序使它们适合于过程挖掘。本文提出的方法是基于准备报警数据的过程挖掘，然后使用合适的过程挖掘算法来提取工厂拓扑结构。提取的拓扑结构表示为常见的Petri网，可用于根本原因分析和发现故障传播路径。文中还讨论了对提取的拓扑进行评价的方法。著名的田纳西伊士曼过程的案例研究表明，所提出的方法的实用性。
英文摘要：
Industrial plants are prone to faults. To notify the operator of a fault occurrence, alarms are utilized as a basic part of modern computer-controlled plants. However, due to the interconnections of different parts of a plant, a single fault often propagates through the plant and triggers a (sometimes large) number of alarms. A graphical plant topology can help operators, process engineers and maintenance experts find the root cause of a plant upset or discover the propagation path of a fault. In this paper, a method is developed to extract plant topology form alarm data. The method is based on process mining, a collection of concepts and algorithms that model a process (not necessarily an engineering one) based on recorded events. The event based nature of alarm data as well as the chronological order of recorded alarms make them suitable for process mining. The methodology developed in this paper is based on preparing alarm data for process mining and then using the suitable process mining algorithms to extract plant topology. The extracted topology is represented by the familiar Petri net which can be used for root cause analysis and discovering fault propagation paths. The methods to evaluate the extracted topology are also discussed. A case study on the well-known Tennessee Eastman process demonstrates the utility of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
