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首页> 外文期刊>Computers in Industry >Using a classifier ensemble for proactive quality monitoring and control: The impact of the choice of classifiers types, selection criterion, and fusion process
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Using a classifier ensemble for proactive quality monitoring and control: The impact of the choice of classifiers types, selection criterion, and fusion process

机译：使用分类器合奏进行主动质量监控和控制：分类器类型，选择标准和融合过程的选择的影响

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摘要

In recent times, the manufacturing processes are faced with many external or internal (the increase of customized product re-scheduling, process reliability...) changes. Therefore, monitoring and quality management activities for these manufacturing processes are difficult. Thus, the managers need more proactive approaches to deal with this variability. In this study, a proactive quality monitoring and control approach based on classifiers to predict defect occurrences and provide optimal values for factors critical to the quality processes is proposed. In a previous work (Noyel et al., 2013), the classification approach had been used in order to improve the quality of a lacquering process at a company plant; the results obtained are promising, but the accuracy of the classification model used needs to be improved. One way to achieve this is to construct a committee of classifiers (referred to as an ensemble) to obtain a better predictive model than its constituent models. However, the selection of the best classification methods and the construction of the final ensemble still poses a challenging issue. In this study, we focus and analyze the impact of the choice of classifier types on the accuracy of the classifier ensemble; in addition, we explore the effects of the selection criterion and fusion process on the ensemble accuracy as well. Several fusion scenarios were tested and compared based on a real-world case. Our results show that using an ensemble classification leads to an increase in the accuracy of the classifier models. Consequently, the monitoring and control of the considered real-world case can be improved.
机译：最近，制造过程面临许多外部或内部（自定义产品重新调度，过程可靠性......）的变化。因此，这些制造过程的监测和质量管理活动很难。因此，管理人员需要更积极主动地处理这种可变性。在本研究中，提出了基于分类器的主动性监测和控制方法，以预测缺陷发生并为质量过程至关重要的因素提供最佳值。在以前的工作中（Noyel等，2013），已经使用了分类方法，以提高公司植物的涂漆过程的质量;获得的结果是有前途的，但需要改善使用的分类模型的准确性。实现这一目标的一种方法是构建分类机构（称为合奏）的委员会，以获得比其组成模型更好的预测模型。然而，选择最佳分类方法和最终集合的构建仍然造成了一个具有挑战性的问题。在这项研究中，我们专注并分析了分类器类型的影响对分类器集合的准确性;此外，我们还探讨了选择标准和融合过程对集合精度的影响。基于真正的案例测试了几种融合情景。我们的结果表明，使用集合分类导致分类器模型的准确性提高。因此，可以改善考虑的真实情况的监测和控制。

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