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.
翻译:网络物理趋同正在为工业经营者开辟新的商业机会; 网络和物理世界的深度一体化需要为巩固新系统和网络工程方法制定丰富的商业议程; 没有丰富和多样化的数据来源及其智能开发能力,这场革命是不可能的,这主要是因为数据将作为促进工业的基本资源,将数据作为促进工业4.0。 这一数据丰富、网络物理和智能工厂环境中最富有成效的研究和实践领域之一是数据驱动的进程监测领域,它应用机器学习方法来进行预测维护应用。 在本文中,我们审查流行的时间序列预测技术以及工业应用背景下的监督机器学习算法,通过改造和预处理包装机操作状态记录的历史工业数据集(来自食品和饮料领域制造厂生产线的真实数据),在我们的方法学中,我们只使用一个有关机器运行状况的单一信号来作出预测,而没有考虑其他操作变量或错误,也没有警告信号,因此,我们用它来定性为“QAagnostic”系统4.0。 在这方面,我们用了一个很有希望的成绩的例子。