从工业警报和事件中提取价值：一种基于探索性数据分析的数据驱动方法。

Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis.

作者信息

Bezerra Aguinaldo, Silva Ivanovitch, Guedes Luiz Affonso, Silva Diego, Leitão Gustavo, Saito Kaku

机构信息

Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil.

Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil.

出版信息

Sensors (Basel). 2019 Jun 20;19(12):2772. doi: 10.3390/s19122772.

DOI:10.3390/s19122772

PMID:31226811

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6631682/

Abstract

Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a data misuse and demands more incisive approaches when analyzing industrial data. Advances in Data Science and Big Data (or more precisely, Industrial Big Data) have been enabling novel approaches in data analysis which can be great allies in extracting hitherto hidden information from plant operation data. Coping with that, this work proposes the use of Exploratory Data Analysis (EDA) as a promising data-driven approach to pave industrial alarm and event analysis. This approach proved to be fully able to increase industrial perception by extracting insights and valuable information from real-world industrial data without making prior assumptions.

摘要

报警和事件日志是一个巨大但潜在的知识来源，在行业中通常被低估。然而，在工业4.0和工业物联网（IIoT）范式推动下的当前大规模数据交换、高效率和强竞争力的格局，无法容忍这种数据滥用，并且在分析工业数据时需要更具洞察力的方法。数据科学和大数据（或更准确地说，工业大数据）的进展使得数据分析中出现了新方法，这些方法在从工厂运行数据中提取迄今隐藏的信息方面可以成为很好的助力。为此，本研究提出使用探索性数据分析（EDA）作为一种有前景的数据驱动方法，为工业报警和事件分析铺平道路。这种方法被证明完全能够通过从实际工业数据中提取见解和有价值的信息来提高工业认知，而无需进行先验假设。