A Measure of the Information Loss for Inspection Point Reduction
Paper i proceeding, 2008

Since the vehicle program in automotive industry gets more and more extensive, the costs related to inspection increase. Therefore, there are needs for more effective inspection preparation. In many situations, a large number of inspection points are measured, despite the fact that only a small subset of points is needed. A method, based on cluster analysis, for identifying redundant inspection points has earlier been successfully tested on industrial cases. Cluster analysis is used for grouping the variables into clusters, where the points in each cluster are highly correlated. From every cluster only one representing point is selected for inspection. In this paper the method is further developed and multiple linear regression is used for evaluating how much of the information that is lost when discarding an inspection point. The information loss can be quantified using an efficiency measure based on linear multiple regression, where the part of the variation in the discarded variables that can be explained by the remaining variables is calculated. This measure can be illustrated graphically and that helps to decide how many clusters that should be formed, i.e. how many inspection points that can be discarded.

information loss

cluster analysis

regression

inspection

variable reduction

Författare

Kristina Wärmefjord

Chalmers, Produkt- och produktionsutveckling, Produktutveckling

Johan Carlson

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Rikard Söderberg

Chalmers, Produkt- och produktionsutveckling, Produktutveckling

ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE2008; Brooklyn, NY; United States; 3 August 2008 through 6 August 2008

PARTS A AND B 693-700

Ämneskategorier

Produktionsteknik, arbetsvetenskap och ergonomi

Bearbetnings-, yt- och fogningsteknik

Tillförlitlighets- och kvalitetsteknik

Sannolikhetsteori och statistik

DOI

10.1115/DETC2008-49708

ISBN

978-079184325-3