Rotary lime kilns are large-scale, energy-intensive unit operations that serve critical functions in a variety of industrial processes including cement production, pyrometallurgy, and kraft pulping. As massive expensive vessels that operate at high temperatures it is imperative from economic, environmental, and safety perspectives to optimize preventative maintenance and production efficiency. To achieve these objectives rotary kilns are increasingly outfitted with more sophisticated sensing technology that can provide additional operating insights. Although increasingly intricate data is collected from industrial operations the extent to which value is extracted from this data is often far from optimal. Our research aims to improve this situation by developing data analytics methods that leverage advanced industrial sensor data to address outstanding process faults. Specifically, this research investigates the use of infrared thermal cameras to detect and diagnose ring formation in rotary lime kilns. The formation of rings is described in literature as the most troublesome problem for lime kiln operation as it can severely limit production, cause millions of dollars in equipment damage, and lead to unplanned shutdowns. In this work we propose a strategy to detect and diagnose ring formation with industrial thermal camera data. While implementing this strategy with an industrial case study we develop a novel process visualization technique and obtain important process insights that enable better data collection. These contributions are presented in this work along with preliminary results from ring detection and diagnosis, on-going challenges, and suggestions for future research directions.