Abstract
In industrial practice, excessive alarms and high alarm rates are mostly generated from unreasonable settings to variable alarm thresholds, which have become the significant causes of impact on operation stability and plant safety. A correlation degree and clustering analysis-based approach was presented to optimize the variable alarm thresholds in this paper. The correlation degrees of variables are first obtained by analyzing correlation relationships among them. Second, the variables are grouped according to the gray correlation coefficients and clustering analysis, given the weight for fault alarm rate (FAR) in each group. An objective function about the FAR, missed alarm rate (MAR), and the maximum acceptable FAR and MAR is then established with variable weight. Eventually, based on an optimization algorithm, the objective function can be optimized for obtaining the optimal alarm threshold. Cases study of the Tennessee Eastman (TE) industrial simulation process and an actual industrial ethylene production process, in comparison to the initial situation, show that the method can effectively reduce FAR according to correlation degrees among variables in the system, and decrease the number of alarms with reduction rates of 40.5% and 35.3%, respectively.
Highlights
With the complexity and refinement of the process of industrial production, the production process is more and more inseparable from real-time monitoring of the system.An alarm management system, as an indispensable part in the safety operation of industrial production, has been paid more and more attention by all walks of life
Cases with more false alarms, a higher false alarm rate (FAR) and a missed alarm rate (MAR) always arise in processes [1], which are mainly caused through the unreasonable threshold settings for variables and ineffective management for alarm systems
It reflects the method could have some impact on the alarm threshold optimization, bring a lower FAR and fewer alarms
Summary
With the complexity and refinement of the process of industrial production, the production process is more and more inseparable from real-time monitoring of the system. Combining FAR and MAR with a correlation coefficient, an off-line method for optimizing thresholds was given to reduce alarms for multi-variables based on time delay [5]. Zhang et al presented an off-line multivariate method based on ROC curve and sensitivity, considering the sensitivity relationship and clustering analysis among variables, to optimize the alarm threshold [27]. Most of the above alarm optimization methods are based on the off-line system optimization, and the results obtained in the corresponding systems are obvious, large or small, effectively optimizing the production process and reducing losses. In view of the problem, a new alarm threshold optimization method is proposed, which uses the correlation degrees among the variables and clustering analysis. Variables with similar influence on the system can be found out through correlation analysis. (2) It could carry on the group sorting according to the intrinsic
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