Information Systems Department, Information Systems School, Campus Sur, Universidad Politécnica de Madrid, 28031 Madrid, Spain.
Department of Geospatial Engineering, School of Surveying Engineering, Campus Sur, Universidad Politécnica de Madrid, 28031 Madrid, Spain.
Sensors (Basel). 2021 Nov 2;21(21):7301. doi: 10.3390/s21217301.
Industry 4.0 is envisioned to transform the entire economical ecosystem by the inclusion of new paradigms, such as cyber-physical systems or artificial intelligence, into the production systems and solutions. One of the main benefits of this revolution is the increase in the production systems' efficiency, thanks to real-time algorithms and automatic decision-making mechanisms. However, at the software level, these innovative algorithms are very sensitive to the quality of received data. Common malfunctions in sensor nodes, such as delays, numerical errors, corrupted data or inactivity periods, may cause a critical problem if an inadequate decision is made based on those data. Many systems remove this risk by seamlessly integrating the sensor nodes and the high-level components, but this situation substantially reduces the impact of the Industry 4.0 paradigm and increases its deployment cost. Therefore, new solutions that guarantee the interoperability of all sensors with the software elements in Industry 4.0 solutions are needed. In this paper, we propose a solution based on numerical algorithms following a predictor-corrector architecture. Using a combination of techniques, such as Lagrange polynomial and Hermite interpolation, data series may be adapted to the requirements of Industry 4.0 software algorithms. Series may be expanded, contracted or completed using predicted samples, which are later updated and corrected using the real information (if received). Results show the proposed solution works in real time, increases the quality of data series in a relevant way and reduces the error probability in Industry 4.0 systems.
工业 4.0 通过将新范式（如网络物理系统或人工智能）纳入生产系统和解决方案，旨在改变整个经济生态系统。这场革命的主要好处之一是通过实时算法和自动决策机制提高了生产系统的效率。然而，在软件层面上，这些创新算法对所接收数据的质量非常敏感。传感器节点的常见故障，如延迟、数值误差、损坏的数据或不活跃期，如果基于这些数据做出不适当的决策，可能会导致严重问题。许多系统通过无缝集成传感器节点和高级组件来消除这种风险，但这种情况会大大降低工业 4.0 范例的影响，并增加其部署成本。因此，需要新的解决方案来保证所有传感器与工业 4.0 解决方案中软件元素的互操作性。在本文中，我们提出了一种基于数值算法的解决方案，该解决方案采用预测校正架构。通过结合拉格朗日多项式和 Hermite 插值等技术，可以将数据序列调整为符合工业 4.0 软件算法的要求。可以使用预测样本扩展、收缩或完成序列，然后使用实际信息（如果收到）更新和更正这些预测样本。结果表明，所提出的解决方案可以实时工作，以相关的方式提高数据序列的质量，并降低工业 4.0 系统中的错误概率。
