A Data-Driven Process Monitoring Approach Based on Evidence Reasoning Rule Considering Interval-Valued Reliability

Abstract

In the process industry, an alarm system is one of the important ways of condition monitoring. Due to the complexity and irregularity of process information in condition monitoring, there are too many false alarms in the current alarm system. In order to solve the problem of designing an alarm system, this paper proposes a multivariate alarm design method based on the evidence reasoning (ER) rule, considering interval-valued reliability, which can make full use of process information to make accurate alarm decisions. Firstly, the referential evidence matrixes (REMs) are constructed based on the training samples of process variables, and the real-time samples of the process variables are converted into alarm evidence by activating the REMs. Alarm evidence is then fused by the ER rule. In this fusion process, in order to better describe the uncertainty of the process information, the reliability of the alarm evidence is characterized by random variables with certain probability distributions, and it can be adjusted in dynamic intervals according to the real-time change of alarm evidence. Finally, the reactor fault case is implemented in the Tennessee Eastman (TE) process, which shows that the adjustment of interval-valued reliability can adapt to the irregular change of process information and obtains consistent alarm results to further improve the accuracy of alarm decisions.