Profiling effects in industrial data mining by non-parametric DOE methods: An application on screening checkweighing systems in packaging operations

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• 作者：George J. Besseris ; ^{besseris@teipir.gr}
• 关键词：Process screening ; Robust design ; Design of Experiments ; Non-linear optimization ; Non-parametric data mining ; Non-linear orthogonal array
• 刊名：European Journal of Operational Research
• 出版年：2012
• 期刊代码：58_03772217
• 类别：et
• 出版时间：1 July, 2012
• 卷：220
• 期：1
• 页码：147-161
• 文件大小：681 K

摘要

There is a growing interest in applying robust techniques for profiling complex processes in industry. In this work, 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 L₉(3⁴) 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.