Chemical process fault detection and trend analysis based on KESN

Fault detection has great significance for chemical process safety with the development of science and technology. The conventional echo state network-based fault detection method does not highlight key fault features, and cannot forecast future fault trend after the occurrence of faults. For the above problems, a chemical process fault detection and trend analysis strategy based on key feature enhanced echo state network (KESN) is proposed. First, dynamic features are extracted by a detecting echo state network. Then, a weighting strategy is designed to enhance key features and increase fault detection rates. After detecting a fault, independent component analysis is utilized to extract independent key features. Future fault trend is forecasted based on the forecasting multi-KESN. Simulation results on the Tennessee Eastman process demonstrate the effectiveness of the proposed method.