Swarm intelligent analysis of independent component and its application in fault detection and diagnosis

An industrial process often has a large number of measured variables, which are usually driven by fewer essential variables. An improved independent component analysis based on particle swarm optimization (PSO-ICA) is involved to extract these essential variables. Process faults can be detected more efficiently by monitoring the independent components. On the basis of this, the diagnosis of faults is reduced to a string matching problem according to the situation of alarm limit violations of independent components. The length of the longest common subsequence (LLCS) between two strings is used to evaluate the difficulty in distinguishing two faults. The proposed method is illustrated by the application to the Tennessee Eastman challenging process.