Optimal PCA-based modeling and fault diagnosis for uneven-length batch processes

Principal component analysis (PCA) has been widely studied and applied in continuous process monitoring and fault diagnosis. However, PCA can't be applied directly in batch processes due to the common multi-dimensionality of data matrix, uneven-length duration. Since the changes in the correlation may be used to indicate changes in the process operation stages, an optimal sub-stage PCA modeling method based on A-unfolding for uneven-length batch process is proposed, in which on the basis of analyzing the characteristics of sub-stage PCA modeling, the optimal model is established and the genetic algorithm is adopted to obtain the solution of optimal model. It is effective for batch processes with limited-runs modeling data and can improve the model precision. Simulation results to an injection molding process shows that the proposed method can partition the sub-stage accurately and it has better ability of process monitoring.