Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, Greece.
Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Po. J. Ma. Arizmendiarrieta 2, 20500 Arrasate-Mondragón, Spain.
Sensors (Basel). 2022 Feb 23;22(5):1734. doi: 10.3390/s22051734.
Manufacturing companies increasingly become "smarter" as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.
由于第四次工业革命，制造企业的智能化程度不断提高。多个传感器用于机器和工人的工业监测，以检测事件，从而改进制造流程，降低相应成本，并提高安全性。多传感器系统产生大量异类数据。数据融合技术通过组合来自不同来源的数据并改进监测系统的结果来解决多模态问题。当前的论文详细回顾了数据融合解决方案的最新技术，包括来自各种类型传感器的数据存储和索引、特征工程和多模态数据集成。该综述旨在为制造预测分析管道的早期阶段提供指导。所审查的文献表明，在融合和预处理中，选择应用于该领域的方法已经超越了现有技术水平。还确定了存在的弱点和差距，这些弱点和差距导致了未来的研究目标。
