Improved Correlation Analysis and Visualization of Industrial Alarm Data

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. 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 the time lag between alarm variables, a correlation color map of the transformed or pseudo data is used to show the cluster of correlated variables with the alarm tags reordered to better group the correlated alarms. Thereafter statistical methods such as singular value decomposition techniques can be applied within each cluster to find the redundant alarm tags. This improved method is shown to be better than the alarm similarity color map when applied in the analysis of industrial alarm data.