Screening web breaks in a pressroom by soft computing

Web breaks are considered as one of the most significant runnability problems in a pressroom. This work concerns the analysis of relation between various parameters (variables) characterizing the paper, printing press, the printing process and the web break occurrence. A large number of variables, 61 in total, obtained off-line as well as measured online during the printing process are used in the investigation. Each paper reel is characterized by a vector x of 61 components. Two main approaches are explored. The first one treats the problem as a data classification task into ”break” and ”non break” classes. The procedures of classifier training, the selection of relevant input variables and the selection of hyper-parameters of the classifier are aggregated into one process based on genetic search. The second approach combines procedures of genetic search based variable selection and data mapping into a low dimensional space. The genetic search process results into a variable set providing the best mapping according to some quality function. The empirical study was performed using data collected at a pressroom in Sweden. The total number of data points available for the experiments was equal to 309. Amongst those, only 37 data points represent the web break cases. The results of the investigations have shown that the linear relations between the independent variables and the web break frequency are not strong. Three important groups of variables were identified, namely Lab data (variables characterizing paper properties and measured off-line in a paper mill lab), Ink registry (variables characterizing operator actions aimed to adjust ink registry) and Web tension. We found that the most important variables are: Ink registry Y LS MD (adjustments of yellow ink registry in machine direction on the lower paper side), Air permeability (characterizes paper porosity), Paper grammage, Elongation MD, and four variables characterizing web tension: Moment mean, Min sliding Mean, Web tension variance, and Web tension mean. The proposed methods were helpful in finding the variables influencing