Health status monitoring of ladle gates in steel plants.

Steel infiltrations in ladle gate and/or breakouts of liquid steel occur occasionally in the continuous casting process leading to severe safety issues and large production loss in steel plants. These are due to the malfunction of ceramic ladle slide gate plates. Today’s standard of their maintenance relies totally on visual inspection by an operator, thus on his experience, training, and alertness in rough environments. In this work, we apply machine learning techniques to monitor the health status of the ceramic ladle plates, by automatically analyzing the force and the position signals measured from the hydraulic cylinder used to open and close the gates. Our goal is to provide a robust and reliable tool for the automatic assessment of the plate health and the prediction of its degradation, in order to increase process stability and safety, optimize the lifetime of the plates and reduce resource waste and CO2 emissions.
Publication Reference
F&E-Konferenz zu Industrie 4.0, Industrie 2025