[EN] Advanced statistical models can help industry to design more economical and rational investment
plans. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing.
Increasingly stringent quality requirements in the automotive industry also require ongoing efforts
in process control to make processes more robust. Robust methods for estimating the quality of
galvanized steel coils are an important tool for the comprehensive monitoring of the performance of the
manufacturing process. This study applies different statistical regression models: generalized linear
models, generalized additive models and classification trees to estimate the quality of galvanized steel
coils on the basis of short time histories. The data, consisting of 48 galvanized steel coils, was divided
into sets of conforming and nonconforming coils. Five variables were selected for monitoring the
process: steel strip velocity and four bath temperatures.
The present paper reports a comparative evaluation of statistical models for binary data using
Receiver Operating Characteristic (ROC) curves. A ROC curve is a graph or a technique for visualizing,
organizing and selecting classifiers based on their performance. The purpose of this paper is to examine
their use in research to obtain the best model to predict defective steel coil probability. In relation to
the work of other authors who only propose goodness of fit statistics, we should highlight one distinctive
feature of the methodology presented here, which is the possibility of comparing the different models
with ROC graphs which are based on model classification performance. Finally, the results are validated
by bootstrap procedures.The authors are indebted to the anonymous referees whose suggestions improved the original manuscript. This work was supported by a grant from PAID-06-08 (Programa de Apoyo a la Investigacion y Desarrollo) of the Universitat Politecnica de Valencia.Debón Aucejo, AM.; García-Díaz, JC. (2012). Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data. Reliability Engineering and System Safety. 100:102-114. https://doi.org/10.1016/j.ress.2011.12.022S10211410