The optimal design of industrial alarm systems based on evidence theory

• Alarm evidence is used to measure uncertainties of the monitored process variable.
• An evidence updating rule is used to combine alarm evidence for generating alarms.
• An optimization method is given to obtain optimal parameters of the alarm system.
• The effectiveness of the system is demonstrated by experiment and industrial case.

This paper presents a procedure for the optimal design of industrial alarm systems based on evidence theory to deal with epistemic and aleatory uncertainties of the monitored process variable. First, the upper and lower fuzzy thresholds are designed, and then the sampled value of the process variable is transformed into a piece of alarm evidence to measure the degrees of uncertainty about whether an alarm should be triggered or not by the sampled value. Second, a linear updating rule of evidence is recursively applied to combine the updated alarm evidence at t−1 step with the incoming alarm evidence at t step to generate the updated alarm evidence at t step. In the process of evidence updating, the weights of evidence for linear combination can be obtained by dynamically minimizing the distance between the updated alarm evidence and the true mode (i.e., “alarm” or “no-alarm”). An alarm decision can then be made according to a pignistic probability transformed from the updated alarm evidence at each time step. Finally, numerical experiments and an industrial case are given to show that the proposed procedure has a better performance than the classical design methods.