Abstract: Entrained flow coal-water slurry( CWS) gasification technology is a clean and efficient coal energy utilization technology,and the gasifier burner is one of the core equipment of the gasification process. The burner will be gradually worn and ablated during the operation process,and there is a potential safety hazard. However,the diagnosis of burner operation status mainly depends on the engineer's experience in addition to the interlock of the burner itself. Qualitative trend analysis( QTA) is a data-driven method based on process history,which is widely used in process monitoring and fault diagnosis. In this paper,QTA method was applied to the data analysis of entrained flow CWS industrial gasification devices to explore the difference between the operating data of gasification device with different burner working time. A diagnosis method based on the difference of data fluctuation was proposed to judge the operation status of burner in time.The trend primitive sequence and the change rate of primitive segmented data of the gasification plant production data of two different burner operating hours were compared. The results show that the growth of burner operating time is mainly reflected in the data fluctuation frequency and amplitude In two respects. The longer the burner operating time is,the greater the fluctuation range of the four sets of data of coal slurry flow,burner pressure difference,slag mouth pressure difference and outlet syngas CH4 content is,and he higher the fluctuation frequency is. In practical applications,the operating status of the burner can be judged by comparing the fluctuation frequency and amplitude of the burner data. Two types of parameters are mainly monitors: the trend primitive sequence and the data change rate of the trend primitive segment. When the primitive sequence frequently shows the opposite trend or when the data change rate increases to several times of the initial parameters,it can be considered that the operating state of the burner has become unstable,and it is necessary to monitor the operation of the burner or replace the burner. The diagnostic method based on the difference of data fluctuations can help the operator to judge the operating status of the burner in time,and realize the combination of computer-aided judgment and industrial big data.