基于传递熵和特征向量中心性分析的全厂故障和扰动筛选
Plant-wide fault and disturbance screening using combined transfer entropy and eigenvector centrality analysis
摘要：
在工业化学加工厂等复杂系统中找到干扰或故障的来源可能是一项艰巨的任务，并消耗大量的工程时间。在许多情况下，系统的消除程序被认为是唯一可行的方法，但可能会导致不希望的过程干扰。从业者希望有强有力的替代方法。本文提出了一种无监督的，数据驱动的方法，根据其影响的大小和新颖性的过程元素进行排名。部分二元转移熵估计用于推断过程元素的加权有向图。特征向量中心性被应用于根据网络节点的整体效果对网络节点进行排名。由于过程元素的排名依赖于依赖于许多连接的集合的新兴属性，因此结果对于个别边缘属性的估计中的错误以及不代表过程的真实因果结构的间接连接的包含是鲁棒的。在多个重叠时间区域上连续计算的过程元件重要性分数的监控图可以辅助初期故障检测。结合信息传输网络的视觉检查的排名结果也是有用的已知故障和干扰的根本原因分析。所提出的方法的软件实现是可用的。
英文摘要：
Finding the source of a disturbance or fault in complex systems such as industrial chemical processing plants can be a difficult task and consume a significant number of engineering hours. In many cases, a systematic elimination procedure is considered to be the only feasible approach but can cause undesired process upsets. Practitioners desire robust alternative approaches. This paper presents an unsupervised, data-driven method for ranking process elements according to the magnitude and novelty of their influence. Partial bivariate transfer entropy estimation is used to infer a weighted directed graph of process elements. Eigenvector centrality is applied to rank network nodes according to their overall effect. As the ranking of process elements rely on emerging properties that depend on the aggregate of many connections, the results are robust to errors in the estimation of individual edge properties and the inclusion of indirect connections that do not represent the true causal structure of the process. A monitoring chart of continuously calculated process element importance scores over multiple overlapping time regions can assist with incipient fault detection. Ranking results combined with visual inspection of information transfer networks is also useful for root cause analysis of known faults and disturbances. A software implementation of the proposed method is available.
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。
