A Correlation Analysis-Based Multivariate Alarm Method With Maximum Likelihood Evidential Reasoning

Correlations among process variables and inconsistencies in alarm decision making are quite common in multivariate alarm analysis, resulting in a large number of false alarms and missed alarms. The greatest challenges in multivariate alarm analysis are therefore analyzing overall correlations among all process variables and making integrated alarm decisions. In this work, a novel correlation analysis-based multivariate alarm method is developed to address these problems. First, a statistical characteristic-driven decision making trial and evaluation laboratory (DEMATEL) is proposed that can analyze the overall correlations among all process variables. Second, the sample space model (SSM) and evidence space model (ESM) can be used to convert process data into reference alarm evidence. Third, online samples are transformed into alarm evidence by matching them with the ESMs and holistically considering the data-level correlations and the evidence-level reliability and weight; the comprehensive alarm evidence is obtained by fusing this matched alarm evidence generated from the information of highly correlated or even colinear variables via maximum likelihood evidential reasoning (MAKER), and thus, more accurate and integrated alarm decisions are made. A real case study shows the superiority of the proposed method, which can therefore be generalized to other multivariate industrial processes.