Kiln Process Fan Vibrations Prediction Based on Machine Learning Models: Application to the Raw Mill Fan

Abstract

Kiln process fans play a crucial role in the cement manufacturing process. This article presents a study on the use of machine learning models to predict kiln process fan vibrations based on process fan running parameters. The study tested three different models, namely, k-nearest neighbors, linear regression, and random forest, and it found that all three could accurately predict fan vibrations. However, the random forest model performed the best due to its ability to handle non-linear relationships. The findings have significant implications for the maintenance and operation of kiln process fans, as predictive maintenance can reduce downtime and improve operational efficiency.