Predicting Wet-End Web Breakage in Paper Mills  Piero Bonissone,
Kai Goebel, and Yu-To Chen  This paper presents a systematic
process for building a predictive model to estimate time-to-
breakage and provide a web break tendency indicator in the wet-
end part of paper making machines. Through successive
information refinement of information gleaned from sensor
readings via data analysis, principal component analysis (PCA),
adaptive network based fuzzy inference system (ANFIS), and
trending analysis, a break tendency indicator was built. Output
of this indicator is the break margin. The break margin is then
interpreted using a stoplight metaphor. This interpretation
provides a more gradual web break sensitivity indicator, since
it uses more classes compared to a binary indicator. By
generating an accurate web break tendency indicator with enough
lead-time, we help in the overall control of the paper making
cycle by minimizing down time and improving productivity.