LAUSR

Rotary kilns are important industrial plants to process material. In this article, we address the problem of detecting material rings formed in alumina rotary kilns, which leads to high waste and substantial economic loss. This necessitates the use of artificial intelligence to help kiln operators detect material rings promptly. We describe a recently developed system that extracts useful features and detects the presence of material rings efficiently and accurately in real time. Our major contribution is a novel feature extraction method based on geometric transformation and peak localization exploiting the domain knowledge. We also contribute a large data set covering many thousand labeled frames for the evaluation of ring detection. On this data set, we demonstrate that our novel feature extraction method outperforms other alternatives in the literature. Our method is also demonstrated as favorable over deep learning approaches. Our system produces an overall accuracy of nearly 96%, which is acceptable for deployment.