An improved kernel exponential discriminant analysis for fault identification of batch process

An improved batch process fault identification approach with kernel exponential discriminant analysis (KEDA) is proposed, in which performance index based on difference degree is given to identify fault classification. This method takes the advantages of both the kernel technology and the exponential discriminant analysis technique. The proposed KEDA method shows powerful ability in dealing with nonlinear, small sample size data and it has a noticeable improvement in classification performance. During the real applications to fault identification, both the normal data model and the fault data model for known faults are established according to the historical data. Then online measurement data is fed into these models to identify the current operation status, i.e., is the system in normal or fault condition, what type of fault occurs, or does new fault appear? Finally, the proposed method is applied to a typical penicillin fermentation process and the simulation results show the effectiveness of the proposed KEDA algorithm and the good performance in fault classification.

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