Combining FAP, MAP and correlation analysis for multivariate alarm thresholds optimization in industrial process

Abstract In the real industrial process, alarm threshold optimization is an important part of alarm system rationalization. If the design of alarm threshold is unreasonable, it would result in nuisance alarms, among which the critical alarms are overwhelmed. In order to alleviate this phenomenon, we propose a method of multivariate alarm thresholds optimization to reduce the nuisance alarms. Firstly, causal relationship between process variables is constructed based on the time delay estimation method, thus we can determine the alarms propagation path and then select the optimized variables. Secondly, in order to guarantee both the process safety and correlation consistency, three factors - false alarm probability (FAP), missed alarm probability (MAP), and the correlation between the alarm information and process information – are combined to establish the objective function of the optimization process for the first time. Then, the optimal thresholds are obtained by the genetic algorithm. Finally, the validity and effectiveness of the developed method are illustrated by the Tennessee Eastman process.

[1]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[2]  Jiandong Wang,et al.  Averaged alarm delay and systematic design for alarm systems , 2010, 49th IEEE Conference on Decision and Control (CDC).

[3]  Yongming Han,et al.  Fault detection of large-scale process control system with higher-order statistical and interpretative structural model , 2015 .

[4]  Nina F. Thornhill,et al.  Finding the Direction of Disturbance Propagation in a Chemical Process Using Transfer Entropy , 2007, IEEE Transactions on Control Systems Technology.

[5]  Nina F. Thornhill,et al.  A practical method for identifying the propagation path of plant-wide disturbances , 2008 .

[6]  Jean-Pierre Vila,et al.  Adaptive threshold computation for CUSUM-type procedures in change detection and isolation problems , 2008, Comput. Stat. Data Anal..

[7]  J. Noyes,et al.  Alarm systems: a guide to design, management and procurement , 1999 .

[8]  Iman Izadi,et al.  Optimal Alarm Signal Processing: Filter Design and Performance Analysis , 2013, IEEE Transactions on Automation Science and Engineering.

[9]  Jia Wang,et al.  A data similarity based analysis to consequential alarms of industrial processes , 2015 .

[10]  Iman Izadi,et al.  Pattern matching of alarm flood sequences by a modified Smith–Waterman algorithm , 2013 .

[11]  Barry M. Wise,et al.  The process chemometrics approach to process monitoring and fault detection , 1995 .

[12]  Yuan Xu,et al.  Integrating probabilistic signed digraph and reliability analysis for alarm signal optimization in chemical plant , 2015 .

[13]  Tongwen Chen,et al.  On correlation analysis of bivariate alarm signals , 2012, 2012 IEEE International Conference on Information and Automation.

[14]  Jie Zhang,et al.  Performance monitoring of processes with multiple operating modes through multiple PLS models , 2006 .

[15]  Sirish L. Shah,et al.  A Framework for Optimal Design of Alarm Systems , 2009 .

[16]  Iman Izadi,et al.  Performance Assessment and Design for Univariate Alarm Systems Based on FAR, MAR, and AAD , 2012, IEEE Transactions on Automation Science and Engineering.

[17]  Iman Izadi,et al.  On expected detection delays for alarm systems with deadbands and delay-timers , 2011 .

[18]  Iman Izadi,et al.  A study on the relation between alarm deadbands and optimal alarm limits , 2011, Proceedings of the 2011 American Control Conference.

[19]  Qunxiong Zhu,et al.  Systematic rationalization approach for multivariate correlated alarms based on interpretive structural modeling and Likert scale , 2015 .

[20]  Fan Yang,et al.  A dynamic alarm management strategy for chemical process transitions , 2014 .

[21]  Sirish L. Shah,et al.  An Introduction to Alarm Analysis and Design , 2009 .

[22]  Raghunathan Rengaswamy,et al.  Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets , 2004, Eng. Appl. Artif. Intell..

[23]  Li Hongguang,et al.  Optimization of process alarm thresholds: A multidimensional kernel density estimation approach , 2014 .

[24]  Richard Thorpe,et al.  A new method for defining and managing process alarms and for correcting process operation when an alarm occurs. , 2004, Journal of hazardous materials.

[25]  Fan Yang,et al.  Correlation analysis of alarm data and alarm limit design for industrial processes , 2010, Proceedings of the 2010 American Control Conference.