Research on Kiln Skin Shedding Working Condition of Rotary Kiln Based on YOLOv11

Accurate identification of abnormal operating conditions in cement rotary kilns is of great importance to improve the clinker quality passage rate, ensure the safe operation of rotary kilns, and improve the production efficiency of enterprises. Due to the high-temperature environment inside the rotary kiln and the characteristics of the equipment itself, traditional contact sensors are difficult to directly monitor the kiln conditions. Based on image processing technology, this paper proposes a YOLOv11based method for detecting abnormal working conditions in cement clinker firing process, considering the typical abnormal working conditions of falling kiln skin in clinker firing process. First, the image data in the rotary kiln is obtained by using advanced visual detection technology, then the image in the rotary kiln is processed by the YOLO algorithm that introduces the attention mechanism to identify possible abnormal areas, and then the data under abnormal working conditions is analyzed to realize the accurate identification of the typical abnormal working conditions of falling kiln skin in the rotary kiln. Finally, experiments are conducted using actual production data from cement plants, and the results show that for 2000 test samples. The abnormal working condition detection method proposed in this paper can achieve an accuracy rate of 83%, providing a feasible method for the accurate detection of abnormal working conditions in the cement clinker firing process.