摘要：Fault prediction is a significant issue for ensuring industrial process safety and reliability. In practical processes, due to complexity and non‐linearity, this leads to many difficulties for process fault prediction. Aiming to improve the fault prediction accuracy in procedure‐oriented systems, a new feedback differential evolution‐optimized extreme learning machine (FDE‐ELM) with a time delay‐based extended finite state machine (TD‐EFSM) approach is proposed. The proposed method is exemplified in the complicated Tennessee Eastman (TE) benchmark process. The results show that the new joint time‐delay EFSM‐based FDE‐ELM shows superiority not only in modelling stability but also in detection sensitivity.
