Multiple timing-driven based extreme learning machine whole process fault prediction and its application

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.