Fault diagnosis is an important method to insure the safety and stability of chemical processes.Principle component analysis (PCA)

one of the typical diagnostic methods

has been widely used in various chemical fault detections.However

PCA is not good at fault diagnosis of complex chemical processes.Artificial immune system (AIS) is an adaptive system inspired by theoretical immunology and observes immune functions

principles and models.Based on the principles of self/non-self discrimination in the immune system

fault diagnosis by using AIS is feasible.The ability of self-learning and self-adaptation makes AIS able to evolve during the online applications.A hybrid diagnostic system combining PCA and AIS was proposed in this paper for early fault diagnosis of chemical processes.A dynamic chemical simulation model was built with Honeywell’s UniSim platform

and the efficiency of the diagnostic system was validated.