Condition Monitoring for Confined Industrial Process Based on Infrared Images by Using Deep Neural Network and Variants

Paper i proceeding, 2020

Some industrial processes take place in confined settings only observable by sensors, e.g. infrared (IR) cameras. Drying processes take place while a material is transported by means of a conveyor through a "black box" equipped with internal IR cameras. While such sensors deliver data at high rates, this is beyond what human operators can analyze and calls for automation. Inspired by numerous implementations monitoring techniques that analyse IR images using deep learning, this paper shows how they can be applied to the confined microwave drying of porous foams, with benchmarking their effectiveness at condition monitoring to conduct fault detection. Convolutional neural networks, derived transfer learning, and deep residual neural network methods are already regarded as cutting-edge and are studied here, using a set of conventional approaches for comparative evaluation. Our comparison shows that state-of-the-art deep learning techniques significantly benefit condition monitoring, providing an increase in fault finding accuracy of up to 48% over conventional methods. Nevertheless, we also found that derived transfer learning and deep residual network techniques do not in our case yield increased performance over normal convolutional neural networks.