报警是及时发现生产过程中异常情况的直接手段,因此科学、高效、准确的智能报警管理对提高生产过程安全性和可操作性至关重要,也是进一步进行优化、控制和故障诊断的基础。本文就智能报警管理这一研究领域进行综述,首先从工业需求上论述报警管理的重要意义,说明报警对于过程安全的影响,指出数量过多和难以辨别是主要难题,并将现实情况与国际标准相对比;然后概述了报警管理的概念和内涵,介绍报警系统改造的全生命周期。其次总结该领域的主要研究问题,并针对单个报警和多个报警的设计展开讨论,揭示误报与漏报、检测速度与鲁棒性的矛盾,并给出常用的解决方法。针对单个报警,通过滤波、延迟和死区,对过程时间序列进行处理,实现报警的快速和准确。针对多个报警,通过多元统计分析全面表征多个变量之间的关系,利用相关性分析和传递熵分析来识别因果报警。最后通过一个实例来说明智能报警管理的过程。

符号有向图SDG(signed directed graph)是描述大规模复杂系统的一种有效方式，通过节点和有向支路表示了系统变量或局部之间的因果影响关系。近年来，关于SDG的研究已经成为热点并已取得许多成果，特别是在安全分析领域得到了重要的应用。本文在概述了SDG方法的产生背景与发展近况的基础上，主要综述了SDG 研究中的若干重要问题，包括SDG模型的数学描述和基于数学模型、流程图和经验知识的三种建模方法，以及SDG在安全评价和故障诊断领域研究和应用的相关成果,总结了相关方法的优缺点，其中的核心问题是推理方法及其效率。最后对SDG技术的发展方向做出了展望，定量信息引入、推理方法、计算机建模等方面都有待于进一步研究。

To improve fault detection reliability, sensor location should be designed according to an optimization criterion with constraints imposed by issues of detectability and identifiability. Reliability requires the minimization of undetectability and false alarm probability due to random factors on sensor readings, which is not only related with sensor readings but also affected by fault propagation. This paper introduces the reliability criteria expression based on the missed/false alarm probability of each sensor and system topology or connectivity derived from the directed graph. The algorithm for the optimization problem is presented as a heuristic procedure. Finally, a boiler system is illustrated using the proposed method.

The problem of multivariate alarm analysis and rationalization is complex and important in the area of smart alarm management due to the interrelationships between variables. The technique of capturing and visualizing the correlation information, especially from historical alarm data directly, is beneficial for further analysis. In this paper, the Gaussian kernel method is applied to generate pseudo continuous time series from the original binary alarm data. This can reduce the influence of missed, false, and chattering alarms. By taking into account time lags between alarm variables, a correlation color map of the transformed or pseudo data is used to show clusters of correlated variables with the alarm tags reordered to better group the correlated alarms. Thereafter correlation and redundancy information can be easily found and used to improve the alarm settings; and statistical methods such as singular value decomposition techniques can be applied within each cluster to help design multivariate alarm strategies. Industrial case studies are given to illustrate the practicality and efficacy of the proposed method. This improved method is shown to be better than the alarm similarity color map when applied in the analysis of industrial alarm data.

This paper is concerned with the fusion of information from process data and process connectivity and its subsequent use in fault diagnosis and process hazard assessment. The Signed Directed Graph (SDG), as a graphical model for capturing process topology and connectivity to show the causal relationships between process variables by material and information paths, has been widely used in root cause and hazard propagation analysis. An SDG is usually built based on process knowledge as described by piping and instrumentation diagrams. This is a complex and experience-dependent task, and therefore the resulting SDG should be validated by process data before being used for analysis. This paper introduces two validation methods. One is based on cross-correlation analysis of process data with assumed time delays, while the other is based on transfer entropy, where the correlation coefficient between two variables or the information transfer from one variable to another can be computed to validate the corresponding paths in SDGs. In addition to this, the relationship captured by data-based methods should also be validated by process knowledge to confirm its causality. This knowledge can be realized by checking the reachability or the influence of one variable on another based on the corresponding SDG which is the basis of causality. A case study of an industrial process is presented to illustrate the application of the proposed methods.