Fault-diagnosis method based on support vector machine and artificial immune for batch process

A new fault-diagnosis method to be used in batch processes based on multi-phase regression is presented to overcome the difficulty arising in the processes due to non-uniform sample data in each phase. Support vector machine is first used for phase identification, and for each phase, improved artificial immune network is developed to analyze and recognize fault patterns. A new cell elimination role is proposed to enhance the incremental clustering capability of the immune network. The proposed method has been applied to glutamic acid fermentation, comparison results have indicated that the proposed approach can better classify fault samples and yield higher diagnosis precision.