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Monitoring nonlinear profiles by piecewise linear approximation
Fan, Shu-Kai S.
Article from Proceeding
International Symposium of Quality Management (ISQM) - Thinking in Quality: The Change and the Unchanged in the Economic Turmoil (Taipei, 6-7 November 2009)
Statistical Process Control (SPC)
Akaike’s Information Criterion (AIC)
Schwarz Information Criterion (SIC)
In many practical situations, the quality of a process or product is better characterized and summarized by the relationship between a response variable and one or more explanatory variable(s). In such situations, there is a relationship between the response variable Y and explanatory variables X that can be represented by a profile. In recent years, profile monitoring has been a popular and fertile field of research in statistical process control (SPC). This paper focuses on segmenting the entire nonlinear profile into several linear approximations that can be monitoring each segment separately. In this light, a new method for determining the number and the location of change points is proposed. Two criterions are used to select the best number of change points to avoid over fitting.
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