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

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          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.

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          Most cited references41

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          A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems

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            Intelligent Manufacturing in the Context of Industry 4.0: A Review

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              Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                20 June 2019
                June 2019
                : 19
                : 12
                : 2772
                Affiliations
                [1 ]Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil; affonso@ 123456dca.ufrn.br
                [2 ]Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil; ivan@ 123456imd.ufrn.br (I.S.); gustavo.leitao@ 123456imd.ufrn.br (G.L.)
                [3 ]School of Sciences and Technology, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil; diego@ 123456ect.ufrn.br
                [4 ]Petróleo Brasileiro S.A., Rio de Janeiro 21941-915, Brazil; kaku@ 123456petrobras.com.br
                Author notes
                [* ]Correspondence: aguinaldo@ 123456ufrn.edu.br ; Tel.: +55-84-988076483
                Author information
                https://orcid.org/0000-0002-0494-8152
                https://orcid.org/0000-0002-0116-6489
                https://orcid.org/0000-0003-2690-1563
                Article
                sensors-19-02772
                10.3390/s19122772
                6631682
                31226811
                f5a49454-5641-433c-a7a1-21a3f905ac05
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 15 March 2019
                : 01 May 2019
                Categories
                Article

                Biomedical engineering
                alarm and event management,data science,exploratory data analysis,industry 4.0,monitoring

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