Sensitivity clustering and ROC curve based alarm threshold optimization

摘要

In industrial practice, to reduce the variable alarm rate and ensure the safety and stability of device production, a variable alarm threshold is optimized by taking into account the receiver operating characteristic (ROC) curve that corresponds to sensitivity clustering, false alarm rate (FAR), and missed alarm rate (MAR). In this paper, the sensitivity value of the variable calculated and the grouping rule recommended by the engineering equipment and materials users association (EEMUA) are first used to cluster the variables into groups and to calculate the relevant weight omega(1). In this approach, in addition to the original weights, omega(1) and omega(2) are the remaining weights, which correspond to the FAR and MAR, respectively. Later, the ROC functional relationship between omega(1) and omega(2) is obtained by the correlativity between the FAR and MAR. An optimized objective function with respect to the FAR, MAR, and original weights is then established, with the clustering weight omega(1) and omega(2) added to the original weights of the FAR and MAR, respectively. Eventually, the objective function is optimized to obtain the optimal alarm threshold by using the particle swarm optimization (PSO) algorithm. The experimental results on the Tennessee Eastman (TE) industrial simulation data show that the proposed method can greatly reduce the FAR according to the variable sensitivity effect on the system, and it can decrease the number of alarms with a reduction rate of 37.8 % in comparison to the initial situation totally. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.