Recent advancements in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. While accuracy is important, focusing primarily on it poses an overfitting danger, exposing manufacturers to risk, ultimately hindering the adoption of these techniques. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty in a cyber-physical manufacturing system (CPMS) scenario. Then, we propose a multi-agent system architecture which leverages probabilistic machine learning as a means of achieving such criteria. We propose possible scenarios for which our proposed architecture is useful and discuss future work. Experimentally, we implement Bayesian Neural Networks for multi-tasks classification on a public dataset for the real-time condition monitoring of a hydraulic system and demonstrate the usefulness of the system by evaluating the probability of a prediction being accurate given its uncertainty. We deploy these models using our proposed agent-based framework and integrate web visualisation to demonstrate its real-time feasibility.

中文翻译：

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信息物理制造系统不确定性下机器学习的多代理系统

预测机器学习的最新进展已使其在制造中的各种用例中得到应用。大多数研究都集中在最大限度地提高预测准确性，而没有解决与之相关的不确定性。虽然准确性很重要，但主要关注它会带来过度拟合的危险，使制造商面临风险，最终阻碍这些技术的采用。在本文中，我们确定了机器学习中的不确定性来源，并建立了机器学习系统的成功标准，以便在网络物理制造系统 (CPMS) 场景中的不确定性下正常运行。然后，我们提出了一种多代理系统架构，它利用概率机器学习作为实现此类标准的一种手段。我们提出了我们提出的架构有用的可能场景，并讨论了未来的工作。在实验上，我们在公共数据集上实施贝叶斯神经网络进行多任务分类，用于液压系统的实时状态监测，并通过评估给定不确定性的预测准确的概率来证明系统的有用性。我们使用我们提出的基于代理的框架部署这些模型，并集成 Web 可视化以证明其实时可行性。