A Novel Safety-Relevant Fault Detection and Assessment Method for Dynamic Process*

Abstract

Process monitoring and fault diagnosis is the key to ensuring process safety and improving product quality. In traditional studies, process safety indicator related fault detection and evaluation have not been fully resolved. To address this problem, a method of maximum information coefficient and slow feature analysis is presented in this paper. The relevance between process and safety variables is calculated by using the maximum information coefficient, and the process variables are divided into strongly safety-related block, weakly safety-related block and safety-unrelated block. Then, the distributed local monitors are established in each block using slow feature analysis method which can identify the process dynamic anomalies. Meanwhile, a new decision fusion method based on logical analysis is proposed to process monitoring and fault grade assessment. Finally, the proposed method is practiced with the Tennessee Eastman Process, and the effectiveness and superiority are proved by comparing with other methods.