Large‐scale dynamic process monitoring based on performance‐driven distributed canonical variate analysis

Abstract

As a typical process monitoring method for the large‐scale industrial process, the distributed principal components analysis (DPCA) needs to be improved because of its rough selection for the variables in each subblock. Moreover, for DPCA, the process dynamic property is ignored and invalid fault diagnosis may occur. Therefore, a performance‐driven distributed canonical variate analysis (DCVA) is proposed. Firstly, with historical fault information, the genetic algorithm is utilized to select appropriate variables for each subblock; secondly, canonical variate analysis is introduced to capture the dynamic information for performance improvement; finally, a novel fault diagnosis method is developed for the DCVA model. Case studies on a numerical example and the Tennessee Eastman benchmark process demonstrate the effectiveness of the proposed model. Highlights A novel fault diagnosis approach based on the distributed CVA model is presented. The dynamic property of process data for each sub‐block is firstly captured by CVA. With the genetic algorithm, the historical fault information is utilized to select appropriate variables in each sub‐block. The superiority of the developed method is validated on the numerical example and the TE process.

Distributed statistical process monitoring based on block‐wise residual generator

“…Abnormalities or faults are then identified as a deviation from the defined normal region above a threshold. 3 Different from first-principle-model-based methods that generate residuals to indicate a fault, 7 data-driven process monitoring approaches mainly focus on modeling normal variation in a dataset given from the NOC. The advances in the field of machine learning continuously motivate novel methods for data-driven process monitoring.…”