Alarm
systems are of paramount importance to the safety and proficiency
of process industries. These systems aim to notify the plant operators
of any possible equipment malfunction or undesired situation. An imperfect
alarm system may announce false alarms in the normal operation mode
or fail to raise alarms in case of some abnormalities. One mechanism
for improving alarm systems is alarm filtering, which has already
been addressed in the literature owing to its popularity in industries.
The focus of previous research was mostly on process variables with
statistically independent variables. This, however, is not aligned
with many practical situations where the acquired variables are originated
from closed-loop control systems. In this paper, we propose a new
method for obtaining optimal filter coefficients by incorporating
the knowledge of plant and control systems while relaxing the independence
assumption. The design objective is to minimize the rates of missed
and false alarms, which are commonly used measures for performance
evaluation of alarm systems. The superiority of the proposed method
over the conventional one is demonstrated via a simulation case study.