Adaptive design of delay timers for non-stationary process variables based on change detection and Bayesian estimation

In industrial alarm systems, delay timers are embedded modules to deal with nuisance alarms. However, most existing approaches for the design of delay timers make an assumption that process variables are stationary distributed, so that designed delay timers may not achieve the desired performance on false alarm rates (FAR) and missed alarm rates (MAR) for non-stationary process variables. Motivated by such a problem, this paper proposes an adaptive approach that updates delay timer parameters to control the number of nuisance alarms. Two main technical issues are addressed. For the first issue of whether delay timer parameters need to be updated, three cases of updating delay timer parameters are formulated according to the changes in alarm durations or intervals and the conditions of process variables. For the second issue of determining time instants to update delay timer parameters, the Bayesian estimation technique is exploited based on confidence intervals of FAR or MAR to be achieved. The proposed approach is illustrated by industrial and numerical examples, showing its necessity via a comparison with conventional delay timers whose parameters are fixed.