Detecting Slag Formations with Deep Convolutional Neural Networks

TL;DR

This paper presents a deep learning approach using convolutional neural networks with convLSTM layers to detect slag formations inside industrial furnace images, improving accuracy and stability for automation.

Contribution

It introduces a novel CNN architecture with convLSTM layers specifically designed for slag detection in challenging furnace environments.

Findings

Achieved sufficient performance for industrial automation

Reduced outlying predictions with convLSTM layers

Lower variance in slag detection over time

Abstract

We investigate the ability to detect slag formations in images from inside a Grate-Kiln system furnace with two deep convolutional neural networks. The conditions inside the furnace cause occasional obstructions of the camera view. Our approach suggests dealing with this problem by introducing a convLSTM-layer in the deep convolutional neural network. The results show that it is possible to achieve sufficient performance to automate the decision of timely countermeasures in the industrial operational setting. Furthermore, the addition of the convLSTM-layer results in fewer outlying predictions and a lower running variance of the fraction of detected slag in the image time series.