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首页> 外文期刊>IEEE transactions on industrial informatics >A Novel Robust Semisupervised Classification Framework for Quality-Related Coupling Faults in Manufacturing Industries
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A Novel Robust Semisupervised Classification Framework for Quality-Related Coupling Faults in Manufacturing Industries

机译：一种新颖的制造业质量相关耦合断层的强大半化分类框架

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An imbalanced number of faulty and normal samples make the traditional supervised classification methods difficult to ensure their classification performance. Accordingly, semisupervised learning methods have recently become hotspots both in academic research and practical application domains. Different from previous schemes, this paper dedicates on the correlations, common features, and specific features among quality-related coupling faults in manufacturing industries. The main innovations are as follows: first, it is the first time to develop a robust semisupervised classification framework for quality-related coupling faults, which integrates semisupervised multitask feature selection and manifold learning; second, manifold structures and local discriminant information of unlabeled and limited labeled faulty samples are sufficiently explored to improve the classification performance; and third, correlations among quality-related coupling faults are accurately captured, which are crucial for understanding the uniqueness and relationships of them at the feature level. The proposed method is finally validated in a representative manufacturing industry, i.e., hot strip mill process, where detailed simulation processes are presented and better classification performance is shown compared with the existing approaches.
机译：错误的错误和正常样本的数量使得传统的监督分类方法难以确保其分类性能。因此，半体验学习方法最近在学术研究和实际应用领域中成为热点。与以前的计划不同，本文致力于制造业质量相关的耦合断层中的相关性，共同特征和特定特征。主要创新如下：首先，第一次开发强大的半体验分类框架，用于质量相关的耦合断层，这集成了半质量的多任务特征选择和流形学习;其次，充分探索了未标记和有限标记有限样品的歧管结构和局部判别信息，以提高分类性能;第三，精确地捕获质量相关的耦合断层之间的相关性，这对于了解它们在特征级别的唯一性和关系至关重要。该方法最终在代表性制造业，即热带轧机过程中验证，其中提出了详细的仿真过程，并与现有方法显示了更好的分类性能。

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