An adaptive spatiotemporal modeling method for curing thermal process

The temperature distribution in the curing oven is a typical distributed parameter system (DPS). Modeling of this kind of system is very difficult as only few sensors are available inside. Besides, thermal behaviors of the oven are time-varying in the directions of space and time. In this paper, an adaptive spatiotemporal modeling method is designed for the curing thermal process. Time-varying spatial basis functions are first obtained under adaptive time/space separation. An online sequential extreme learning machine (OS-ELM) is further developed for online modeling of the time-varying dynamics in time direction. Finally, the temperature distribution of the oven can be estimated by the adaptive spatiotemporal model. Simulation results demonstrate the superior of the proposed modeling method.