利用知识嵌入式学习方法改进用于智能制造的机器学习模型的可靠性

IF 5.9 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Farzam Farbiz, Saurabh Aggarwal, Tomasz Karol Maszczyk, Mohamed Salahuddin Habibullah, Brahim Hamadicharef
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引用次数: 0

摘要

机器学习模型在智能制造领域发挥着至关重要的作用,它彻底改变了工业自动化,从而提高了生产率和产品质量。然而,这些模型的可靠性往往面临着数据漂移、概念漂移、对抗性攻击和模型复杂性增加等因素的挑战。为应对这些挑战,本文提出了一种名为 "可靠性改进机器学习"(RIML)的新方法,该方法通过在应用领域内易于验证和评估的二次输出,将先验知识纳入机器学习管道,从而充分利用先验知识。RIML 以知识嵌入式机器学习(KML)框架为基础,通过修改模型的架构而与传统策略有所不同。在其实施过程中,引入了额外的层次,专门用于识别和摒弃错误分类的案例,以提高模型的可靠性。通过模拟数据集和三个实际案例研究,即一般步行/跑步场景、使用地铁数据集的行业相关案例和气体检测智能制造应用,成功展示了 RIML 的功效。这些令人鼓舞的结果凸显了 RIML 显著减少误分类的能力,从而提高了模型在各种真实世界场景中的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Reliability-improved machine learning model using knowledge-embedded learning approach for smart manufacturing

Reliability-improved machine learning model using knowledge-embedded learning approach for smart manufacturing

Machine learning models play a crucial role in smart manufacturing by revolutionizing industrial automation so as to boost productivity and product quality. However, the reliability of these models often faces challenges from factors such as data drift, concept drift, adversarial attacks, and increasing model complexity. In addressing these challenges, this paper proposes a novel approach called Reliability Improved Machine Learning (RIML), which leverages on prior knowledge by incorporating it into the machine learning pipeline through a secondary output that is easily verifiable and assessable within the application domain. Built upon the Knowledge-embedded Machine Learning (KML) framework, RIML differs from conventional strategies by modifying the model’s architecture. In its implementation, additional layers were introduced, specifically designed to identify and discard misclassified cases to improve the model’s reliability. RIML’s efficacy was successfully demonstrated through a simulated dataset and three real use-case studies, namely, a general walk/run scenario, an industry-related case using metro railway dataset, and a smart manufacturing application on gas detection. The promising results highlighted RIML’s ability to significantly reduce misclassifications, thereby enhancing model reliability in diverse real-world scenarios.

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来源期刊
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing 工程技术-工程:制造
CiteScore
19.30
自引率
9.60%
发文量
171
审稿时长
5.2 months
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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