Optimization of process alarm thresholds: A multidimensional kernel density estimation approach
Optimization of process alarm thresholds: A multidimensional kernel density estimation approach
复制标题
DOI：
10.1002/prs.11658
复制
发表时间：
2014-09
影响因子：
1
通讯作者：
Zang Hao;Liu Hongguang
中科院分区：
文献类型：
--
作者：
Zang Hao;Liu Hongguang
报警系统的合理性极大地影响着过程装置的安全性和经济性，这就必然要求对过程报警阈值进行优化。在这项工作中，首先，我们从概率论的角度分析了漏警和虚警的可分配原因，然后制定相应的基于贝叶斯推理的计算指标。然后，我们最小化漏警概率（MAP）和虚警概率（FAP）在一个多维空间，其中核密度估计方法被调用来估计联合概率密度函数使用过程的历史数据。在密度函数的基础上建立了多变量过程报警阈值优化的数学模型，并采用梯度下降算法来获得合理的报警阈值。工业应用表明，该方法有效地降低了装置的MAP，并将FAP降低到一个相对合理的水平。© 2014美国化学工程师学会Process SafProg 33：292-298，2014
Rationality of alarm systems enormously impacts safety and economic performances of process plants, which definitely demands for process alarm threshold optimization. In this work, first, we analyze the assignable causes of missed alarms and false alarms from the probability theory perspectives before formulating corresponding calculation metrics based on Bayesian Inference. Then, we minimize missed alarm probability (MAP) and false alarm probability (FAP) in a multidimensional space, where the kernel density estimation method is invoked to estimate joint probability density functions using process historical data. Mathematical models associated with multivariable process alarm threshold optimization are established on the basis of density functions, and gradient descent algorithms are employed to achieve advisable alarm thresholds. An industrial application shows that this approach effectively reduces MAP of the plant, as well as lowers FAP to a relatively reasonable level. © 2014 American Institute of Chemical Engineers Process Saf Prog 33: 292–298, 2014