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.
Note to Practitioners
—Multivariate industrial processes generally have a large number of process variables, and with the rapid transfer of energy, material, and information, these process variables interact with each other or are even colinear. The focus of this study is to develop a multivariate alarm method for the correlation analysis of process variables and fusion of complementary, redundant and contradictory process information. The information fusion concept takes the place of the conventional alarm mechanism. From the perspective of the precise characterization of process information, process data are transformed into alarm evidence instead of alarm data. In addition, the proposed method can fully consider the overall correlations among all process variables and fuse each piece of process variable information to yield correct and integrated alarm decision results. It is noted that the information fusion concept is universal and can be extended to other real multivariate industrial processes.