Learning-Based Adaptive Spatiotemporal Modeling of Industrial Distributed Processes

This paper proposed a learning-enabled approach for adaptive spatiotemporal modeling of industrial distributed processes. Within the framework of Karhunen-Loéve (KL) separation, the spatial basis functions (SBF) are updated online in a forgetful learning mode to capture spatial dynamics. Then, the temporal model is also updated iteratively in a forgetting mode to adjust temporal dynamics. Finally, the predicted spatiotemporal state is obtained via Time/Space synthesis. This dual forgetting mechanism embedded in the model can adaptively track the spatiotemporal dynamic changes, thus achieving better modeling effects. The experimental validation of distributed thermal processes in battery operation demonstrates the modeling efficacy of the designed learning approach.