A spatiotemporal prediction approach for a 3D thermal field from sensor networks

Abstract

Thermal fields exist widely in engineering systems and are critical for engineering operation, product quality and system safety in many industries. An accurate prediction of thermal field distribution, that is, acquiring any location of interest in a thermal field at the present and future time, is essential to provide useful information for the surveillance, maintenance, and improvement of a system. However, thermal field prediction using data acquired from sensor networks is challenging due to data sparsity and missing data problems. To address this issue, we propose a field spatiotemporal prediction approach based on transfer learning techniques by studying the dynamics of a 3 D thermal field from multiple homogeneous fields. Our model characterizes the spatiotemporal dynamics of the local thermal field variations by considering the spatiotemporal correlation of the fields and harnessing the information from homogeneous fields to acquire an accurate thermal field distribution in the future. A real case study of thermal fields during grain storage is conducted to validate our proposed approach. Grain thermal field prediction results provide a deep insight of grain quality during storage, which is helpful for the manager of grain storage to make further decisions about grain quality control and maintenance.