Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, 6 Rue Richard Coudenhove-Kalergi, L-1359 Luxembourg, Luxembourg.
Sensors (Basel). 2020 Dec 18;20(24):7273. doi: 10.3390/s20247273.
With the Industry 4.0 paradigm comes the convergence of the Internet Technologies and Operational Technologies, and concepts, such as Industrial Internet of Things (IIoT), cloud manufacturing, Cyber-Physical Systems (CPS), and so on. These concepts bring industries into the big data era and allow for them to have access to potentially useful information in order to optimise the Overall Equipment Effectiveness (OEE); however, most European industries still rely on the Computer-Integrated Manufacturing (CIM) model, where the production systems run as independent systems (i.e., without any communication with the upper levels). Those production systems are controlled by a Programmable Logic Controller, in which a static and rigid program is implemented. This program is static and rigid in a sense that the programmed routines cannot evolve over the time unless a human modifies it. However, to go further in terms of flexibility, we are convinced that it requires moving away from the aforementioned old-fashioned and rigid automation to a ML-based automation, i.e., where the control itself is based on the decisions that were taken by ML algorithms. In order to verify this, we applied a time series classification method on a scale model of a factory using real industrial controllers, and widened the variety of parts the production line has to treat. This study shows that satisfactory results can be obtained only at the expense of the human expertise (i.e., in the industrial process and in the ML process).
随着工业 4.0 范式的到来，互联网技术和运营技术融合在一起，出现了工业物联网（IIoT）、云制造、信息物理系统（CPS）等概念。这些概念将工业带入大数据时代，使它们能够获取潜在有用的信息，以优化整体设备效率（OEE）；然而，大多数欧洲工业仍然依赖计算机集成制造（CIM）模型，其中生产系统作为独立系统运行（即，与上层没有任何通信）。这些生产系统由可编程逻辑控制器控制，其中实现了静态和刚性程序。该程序在某种意义上是静态和刚性的，即编程例程不能随着时间的推移而演变，除非人为修改它。然而，为了在灵活性方面更进一步，我们坚信需要摆脱上述老式和刚性的自动化，转向基于机器学习的自动化，即控制本身基于机器学习算法做出的决策。为了验证这一点，我们在使用真实工业控制器的工厂规模模型上应用了时间序列分类方法，并拓宽了生产线需要处理的零件种类。这项研究表明，只有牺牲人类专业知识（即工业过程和机器学习过程），才能获得令人满意的结果。
