A novel quality-relevant fault detection method based on MICA-SOM multi-subspace partitioning for non-Gaussian industrial processes

Abstract

BackgroundEnsuring safe production and process quality is important for chemical processes. Therefore, a multi-subspace partitioning method based on modified independent component analysis (MICA) and self-organizing map (SOM) is proposed for quality-relevant fault detection. MICA can extract several independent components from the input data, where the mixing matrix implies relationship between independent components and input data. MethodsAccording to the mixing matrix and SOM, the measured variables are classified into quality-relevant variables and quality-irrelevant variables. The quality-relevant variables are mapped into quality-relevant independent component space (Qr-ICS) and the quality-irrelevant independent component space (Qir-ICS) by modified independent component regression and feature decomposition. Then, Qr-ICS is used for quality-relevant fault detection. Quality-irrelevant variables are divided into several quality-irrelevant subspaces based on the correlations among variables. MICA models are established for the quality-irrelevant subspaces. Combined with the Qir-ICS, quality-irrelevant fault detection can be realized. Significant FindingsThe application in the TE process shows that the proposed method has better quality-relevant monitoring reliability and quality-irrelevant monitoring performance than several comparison methods. The application in an actual industrial verifies the advantages and effectiveness of the proposed method over several traditional methods in practical applications.