A multiple timing-driven modeling method is an effective way for fault prediction and state evaluation of complex system

in which the artificial neural network is an effective data-driven modeling tool to deal with the nonlinear problems. Recently

it has been widely concerned on the multiple timing-driven modeling problems. In the paper

from the perspective of the whole process

the k-nearest neighbor mutual information method is firstly used to reduce the dimension of the multiple timing variables and calculate the correlation among the variables

so as the select the characteristic variable. Second

an improved trend analysis method is proposed to monitor the system state in real time and segment the system operation state. Finally

aiming at the potential fault stage

extreme learning machine (ELM) neural network is used for fault prediction. Through the simulation experiment on penicillin fermentation process

the results verify the effectiveness of the proposed method.