Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 2V4, Canada.
ISA Trans. 2012 Jul;51(4):499-506. doi: 10.1016/j.isatra.2012.03.005. Epub 2012 Apr 12.
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.
多元报警分析和合理化问题在智能报警管理领域中非常复杂和重要，因为变量之间存在相互关系。捕捉和可视化相关信息的技术，特别是直接从历史报警数据中获取，有利于进一步分析。本文应用高斯核方法从原始二进制报警数据中生成伪连续时间序列。这可以减少漏报、误报和误报的影响。考虑到报警变量之间的时间滞后，使用变换或伪数据的相关色图显示相关变量的簇，并重新排序报警标签，以便更好地对相关报警进行分组。此后，可以轻松找到相关和冗余信息，并将其用于改进报警设置；并且可以在每个簇内应用奇异值分解技术等统计方法来帮助设计多元报警策略。给出了工业案例研究来说明所提出方法的实用性和有效性。在分析工业报警数据时，该改进方法的应用效果优于报警相似性色图。
