Multivariate Statistical Monitoring of Key Operation Units of Batch Processes Based on Time-Slice CCA

A modern batch process can be characterized by a large scale and multiple operation units, and local fault detection for the key units of such a batch process is imperative. A time-slice canonical correlation analysis (CCA)-based multivariate statistical monitoring scheme for the key operation units of batch processes is proposed. First, the three-way batch process data are unfolded into the time-slice data. Second, CCA modeling is performed at each time instant to explore the correlation between the key units and the entire process. Then, a fault detection residual is generated and monitoring statistics are constructed. The statistics discriminate both the process status and the type of a detected fault, a fault relevant or irrelevant to the other units. The feasibility and superiority of the proposed fault detection scheme are demonstrated by case studies on a numerical example and an industrial injection molding process.