Fault diagnosis for batch processes based on improved MFDA

Due to starting conditions and exotic environment of each batch run are different, their lengths are unequal. Moreover, batch data of measured parameters are not complete until the end of its operation. The shortcomings of conventional multiway Fisher discriminant analysis (MFDA) are that all batch lengths should be equal and that future trajectory of the current batch must be estimated to allow on-line fault diagnosis. Therefore, conventional MFDA easily leads to false fault diagnosis. In order to overcome those drawbacks and enhance the diagnostic performance, an improved method of fault diagnosis, improved multiway Fisher discriminant analysis (IMFDA), is proposed. The diagnostic ability of proposed method is demonstrated by application in the simulated fed-batch penicillin fermentation. Application results show that this method is very efficient.