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Profiling effects in industrial data mining by non-parametric DOE methods: an application on screening checkweighing systems in packaging operations. (English) Zbl 1253.90226
Summary: There is a growing interest in applying robust techniques for profiling complex processes in industry. We present an approach for analyzing fractional-factorial data by building distribution-free models suitable for dealing with replicated trials in search of non-linear effects. The technique outlined in this article is synthesized by implementing four key elements: (1) the data collection efficiency of non-linear fractional factorial designs, (2) the data compression capabilities of rank-sums for repetitive sampling schemes, (3) the rank-ordering as a means to transform data, and (4) the non-parametric screening for prominent effects where the normality and sparsity assumptions are waived. The technique is tested on four controlling factors for profiling the packaging weighing operations of a pharmaceutical enterprise. The robust data mining of repeated trials based on an \(L_{9}(3^{4})\) orthogonal array scheme with embedded uncontrolled noise is discussed extensively. The technique has been subjected to quality control as it is tested with well-defined artificial data. Concluding remarks involve contrasting this new technique with mainstream competing schemes.

MSC:
90C90 Applications of mathematical programming
62P30 Applications of statistics in engineering and industry; control charts
62G99 Nonparametric inference
62K25 Robust parameter designs
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