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工业 4.0 的数据分析方法和工具:系统文献回顾与分类。

Data Science Methods and Tools for Industry 4.0: A Systematic Literature Review and Taxonomy.

机构信息

Applied Computing Graduate Program, University of Vale do Rio dos Sinos, 950, Unisinos Av., São Leopoldo 93022-000, RS, Brazil.

HT Micron Semiconductors S.A., 1550, Unisinos Av., São Leopoldo 93022-750, RS, Brazil.

出版信息

Sensors (Basel). 2023 May 23;23(11):5010. doi: 10.3390/s23115010.

Abstract

The Fourth Industrial Revolution, also named Industry 4.0, is leveraging several modern computing fields. Industry 4.0 comprises automated tasks in manufacturing facilities, which generate massive quantities of data through sensors. These data contribute to the interpretation of industrial operations in favor of managerial and technical decision-making. Data science supports this interpretation due to extensive technological artifacts, particularly data processing methods and software tools. In this regard, the present article proposes a systematic literature review of these methods and tools employed in distinct industrial segments, considering an investigation of different time series levels and data quality. The systematic methodology initially approached the filtering of 10,456 articles from five academic databases, 103 being selected for the corpus. Thereby, the study answered three general, two focused, and two statistical research questions to shape the findings. As a result, this research found 16 industrial segments, 168 data science methods, and 95 software tools explored by studies from the literature. Furthermore, the research highlighted the employment of diverse neural network subvariations and missing details in the data composition. Finally, this article organized these results in a taxonomic approach to synthesize a state-of-the-art representation and visualization, favoring future research studies in the field.

摘要

第四次工业革命,也称为工业 4.0,正在利用几个现代计算领域。工业 4.0 包括制造设施中的自动化任务,这些任务通过传感器生成大量数据。这些数据有助于解释工业运营情况,以便进行管理和技术决策。数据科学支持这种解释,因为它拥有广泛的技术手段,特别是数据处理方法和软件工具。在这方面,本文对这些方法和工具在不同工业领域的应用进行了系统的文献综述,考虑了对不同时间序列水平和数据质量的调查。系统的方法学最初从五个学术数据库中筛选了 10456 篇文章,其中 103 篇被选入语料库。因此,该研究回答了三个一般问题、两个重点问题和两个统计问题,以形成研究结果。结果表明,该研究发现了 16 个工业领域、168 种数据科学方法和 95 种软件工具,这些方法和工具在文献研究中得到了探索。此外,该研究还强调了在数据组成中使用了各种神经网络子变体和缺失的细节。最后,本文以分类法的方式组织了这些结果,以综合出一个最新的表示和可视化,有利于该领域的未来研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bea0/10255695/e0345c5091d9/sensors-23-05010-g001.jpg

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