智能工厂中包装机停机预测的不可知学习
Agnostic Learning for Packing Machine Stoppage Prediction in Smart Factories
摘要：
网络物理融合为工业运营商开辟了新的商机。网络和物理世界的深度整合需要建立一个丰富的业务议程，以巩固新的系统和网络工程方法。如果没有丰富和异构的数据源以及智能开发的能力，这场革命就不可能实现，这主要是因为数据将成为推动工业4.0的基本资源。从这种数据丰富的网络物理智能工厂环境中出现的最富有成效的研究和实践领域之一是数据驱动的过程监控领域，该领域应用机器学习方法来实现预测性维护应用。在本文中，我们研究流行的时间序列预测技术以及监督机器学习算法在工业4.0的应用背景下，通过转换和预处理包装机的操作状态记录的历史工业数据集（来自食品和饮料领域的制造工厂的生产线的真实数据）。在我们的方法中，我们只使用关于机器运行状态的单个信号来进行预测，而不考虑其他运行变量或故障和警告信号，因此其特征为“不可知”。在这方面，结果表明，所采用的方法在三个目标用例上实现了相当有前途的性能。
英文摘要：
The cyber-physical convergence is opening up new business opportunities for industrial operators. The need for deep integration of the cyber and the physical worlds establishes a rich business agenda towards consolidating new system and network engineering approaches. This revolution would not be possible without the rich and heterogeneous sources of data, as well as the ability of their intelligent exploitation, mainly due to the fact that data will serve as a fundamental resource to promote Industry 4.0. One of the most fruitful research and practice areas emerging from this data-rich, cyber-physical, smart factory environment is the data-driven process monitoring field, which applies machine learning methodologies to enable predictive maintenance applications. In this paper, we examine popular time series forecasting techniques as well as supervised machine learning algorithms in the applied context of Industry 4.0, by transforming and preprocessing the historical industrial dataset of a packing machine's operational state recordings (real data coming from the production line of a manufacturing plant from the food and beverage domain). In our methodology, we use only a single signal concerning the machine's operational status to make our predictions, without considering other operational variables or fault and warning signals, hence its characterization as ``agnostic''. In this respect, the results demonstrate that the adopted methods achieve a quite promising performance on three targeted use cases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
