Early transient fault diagnosis of distillation column based on principle component analysis and adaptive neuro-fuzzy inference system

A novel online algorithm for early fault detection and diagnosis based on statistic method and adaptive neuro-fuzzy inference system (ANFIS) is developed. Principal component analysis (PCA) was used to extract feature vectors of data set of complex chemical plant. The most superior features are fed into ANFIS to identify different abnormal cases. Ability and at the same time the simplicity and rapidity has significantly enhanced. Furthermore the advantage is that no model or structural information about the system is needed. This proposed approach has been implemented on a simulated nonlinear MIMO distillation column.