ML2MAS:用于智能制造优化的多智能体强化学习和BNNs-GAN集成框架

Shadia Yahya Baroud, Nor Adnan Yahaya
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引用次数: 0

摘要

在先进技术的推动下,智能制造的兴起改变了传统的生产流程,为运营效率和预测性维护(PdM)创造了新的机会。本研究介绍了ML2MAS,这是一个创新框架,将机器学习(ML)模型集成到多智能体强化学习(MAS-RL)系统中,以增强制造环境中的PdM能力。ML2MAS结合了贝叶斯神经网络(BNNs)处理高维数据和量化预测不确定性,以及生成对抗网络(GANs)合成数据生成,解决了有限标记数据集的挑战,提高了模型的鲁棒性。将这些组件集成到MAS中,使PdM能够做出分散的实时决策。案例研究的经验结果表明,该方法有了实质性的改进,在预测准确性方面达到了99%的f1分,并显著降低了维护成本。提议的ML2MAS框架确保了一个内聚的、自适应的PdM解决方案,并有助于更可持续和高效的制造运营。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ML2MAS: a multi-agent reinforcement learning and BNNs-GAN integration framework for smart manufacturing optimization
The rise of smart manufacturing, driven by advanced technological enablers, has transformed traditional production processes, creating new opportunities for operational efficiency and predictive maintenance (PdM). This study introduces ML2MAS, an innovative framework that integrates machine learning (ML) models within a multi-agent reinforcement learning (MAS-RL) system to enhance PdM capabilities in manufacturing environments. ML2MAS combines Bayesian Neural Networks (BNNs) for handling high-dimensional data and quantifying prediction uncertainty alongside Generative Adversarial Networks (GANs) for synthetic data generation, addressing the challenge of limited labelled datasets and improving model robustness. Integrating these components within MAS enables PdM to make decentralized, real-time decisions. Empirical results from a case study demonstrate substantial improvements, achieving a 99 % F1-score in predictive accuracy and notable reductions in maintenance costs. The proposed ML2MAS framework ensures a cohesive, adaptive PdM solution and contributes to more sustainable and efficient manufacturing operations.
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CiteScore
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