Fault diagnosis of batch processes using discriminant model

A new statistical online diagnosis method for a batch process is proposed. The proposed method consists of two phases: offline model building and online diagnosis. The offline model building phase constructs an empirical model, called a discriminant model, using various past batch runs. When a fault of a new batch is detected, the online diagnosis phase is initiated. The behaviour of the new batch is referenced against the model, developed in the offline model building phase, to make a diagnostic decision. The diagnosis performance of the proposed method is tested using a dataset from a polyvinyl chloride batch process. It has been shown that the proposed method outperforms existing principle components analysis-based diagnosis methods, especially at the onset of a fault.