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.
翻译:预测机器学习的最近进展导致其应用于制造中的各种使用案例。大多数研究侧重于在不解决与之相关的不确定性的情况下实现预测准确性最大化。虽然准确性很重要,但主要侧重于它构成过度适应的危险,使制造商面临风险,最终阻碍采用这些技术。在本文件中,我们确定机器学习的不确定性来源,确定机器学习系统的成功标准,以便在网络物理制造系统(CPMS)设想的不确定性下顺利运行。然后,我们提出一个多试剂系统结构,利用概率机器学习作为实现这些标准的手段。我们提出我们拟议结构有用的可能设想,并讨论今后的工作。我们实验性地在水力系统实时状况监测的公共数据集上实施多种任务分类的Bayesian神经网络,通过评估预测准确性的可能性来证明该系统的效用。我们利用我们拟议的代理框架来部署这些模型,并结合网络直观性来展示其实时可行性。