Similar Batch Process Monitoring With Orthogonal Subspace Alignment

Abstract

In industrial batch productions, operating conditions should be altered frequently to meet the ever-changing market requirements. Although changed conditions will result in variant data properties, those generated target batches share similar running properties with source batches. To effectively and efficiently monitor those similar processes, a novel multiphase orthogonal subspace manifold alignment diagram is proposed in this work. With the extracted orthogonal locality preserving projection subspace manifolds from two corresponding batches, the phase-based Procrustes analysis is conducted to make a source-target coordinate system for similarity matching and alignment. On the basis of that, the new batch matches the old data and the multiphase k -nearest neighborhood monitoring charts can be developed for monitoring the new process within the historical domain knowledge. Finally, a systematic modeling and monitoring framework has been established, with which the laborious efforts for repetitive modeling can be reduced, and the monitoring performance can be improved. For industrial validation, the feasibility and superiority of the general diagram are demonstrated on the fed-batch penicillin fermentation process and the semiconductor manufacturing process.