用多目标优化选关键特征，提升智能制造系统故障检测能力。
A Multi-objective Optimization Approach for Feature Selection in Gentelligent Systems
- 设计混合算法，一次运行同时优化特征选择与分类效果。
- 在两个真实工业数据集上验证，显著提升故障检测性能。
- 适合制造业智能系统开发者，用于应对多目标优化难题。
将人工智能等先进技术融入制造流程正受到广泛关注，推动了智能化系统的兴起，以提升效率与自动化水平。本文提出“Gentelligent系统”概念，指融合组件固有信息（类比生物信息学中的基因，其中制造工序类比染色体）及自动机制的系统。通过实施可靠的故障检测方法，制造商可提升产品质量、增加良品率并降低生产成本。为此，我们提出一种基于支配关系的多目标进化算法混合框架，可在单次运行中探索帕累托最优解，实现特征选择与分类性能的同步优化。该方法能有效监控多种制造工序，解决需同时最小化的多重冲突目标问题。为验证模型有效性，我们采用来自不同工业领域的两个真实数据集进行测试，结果表明该方法具备良好的泛化能力和实际应用效果。
原文摘要 · Abstract (English)
The integration of advanced technologies, such as Artificial Intelligence (AI), into manufacturing processes is attracting significant attention, paving the way for the development of intelligent systems that enhance efficiency and automation. This paper uses the term "Gentelligent system" to refer to systems that incorporate inherent component information (akin to genes in bioinformatics-where manufacturing operations are likened to chromosomes in this study) and automated mechanisms. By implementing reliable fault detection methods, manufacturers can achieve several benefits, including improved product quality, increased yield, and reduced production costs. To support these objectives, we propose a hybrid framework with a dominance-based multi-objective evolutionary algorithm. This mechanism enables simultaneous optimization of feature selection and classification performance by exploring Pareto-optimal solutions in a single run. This solution helps monitor various manufacturing operations, addressing a range of conflicting objectives that need to be minimized together. Manufacturers can leverage such predictive methods and better adapt to emerging trends. To strengthen the validation of our model, we incorporate two real-world datasets from different industrial domains. The results on both datasets demonstrate the generalizability and effectiveness of our approach.
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