  The cigarette draw resistance monitoring method is incomplete and single, and
the lacks correlation analysis and preventive modeling, resulting in
substandard cigarettes in the market. To address this problem without
increasing the hardware cost, in this paper, multi-indicator correlation
analysis is used to predict cigarette draw resistance. First, the monitoring
process of draw resistance is analyzed based on the existing quality control
framework, and optimization ideas are proposed. In addition, for the three
production units, the cut tobacco supply (VE), the tobacco rolling (SE), and
the cigarette-forming (MAX), direct and potential factors associated with draw
resistance are explored, based on the linear and non-linear correlation
analysis. Then, the correlates of draw resistance are used as inputs for the
machine learning model, and the predicted values of draw resistance are used as
outputs. Finally, this research also innovatively verifies the practical
application value of draw resistance prediction: the distribution
characteristics of substandard cigarettes are analyzed based on the prediction
results, the time interval of substandard cigarettes being produced is
determined, the probability model of substandard cigarettes being sampled is
derived, and the reliability of the prediction result is further verified by
the example. The results show that the prediction model based on correlation
analysis has good performance in three months of actual production.

Applications;Methodology