Title of article :
Improved correlation analysis and visualization of industrial alarm data
Author/Authors :
Yang، نويسنده , , F. and Shah، نويسنده , , S.L. and Xiao، نويسنده , , D. and Chen، نويسنده , , T.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Abstract :
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.
Keywords :
Correlation color map , Alarm management , Gaussian Kernel , Clustering , Pseudo data , Visualization
Journal title :
ISA TRANSACTIONS
Journal title :
ISA TRANSACTIONS