{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T04:53:13Z","timestamp":1779252793907,"version":"3.51.4"},"reference-count":60,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,20]],"date-time":"2019-06-20T00:00:00Z","timestamp":1560988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s19122772","type":"journal-article","created":{"date-parts":[[2019,6,20]],"date-time":"2019-06-20T10:49:59Z","timestamp":1561027799000},"page":"2772","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0494-8152","authenticated-orcid":false,"given":"Aguinaldo","family":"Bezerra","sequence":"first","affiliation":[{"name":"Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0116-6489","authenticated-orcid":false,"given":"Ivanovitch","family":"Silva","sequence":"additional","affiliation":[{"name":"Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2690-1563","authenticated-orcid":false,"given":"Luiz Affonso","family":"Guedes","sequence":"additional","affiliation":[{"name":"Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diego","family":"Silva","sequence":"additional","affiliation":[{"name":"School of Sciences and Technology, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gustavo","family":"Leit\u00e3o","sequence":"additional","affiliation":[{"name":"Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaku","family":"Saito","sequence":"additional","affiliation":[{"name":"Petr\u00f3leo Brasileiro S.A., Rio de Janeiro 21941-915, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,20]]},"reference":[{"key":"ref_1","unstructured":"Brian Hartmann, W.P.K., and Narayanan, S. (2019, May 03). Digital Manufacturing: The Revolution will Be Virtualized. Available online: https:\/\/www.mckinsey.com\/business-functions\/operations\/our-insights\/digital-manufacturing-the-revolution-will-be-virtualized."},{"key":"ref_2","unstructured":"van Aardt, D. (2019, May 03). More Data Is Only Useful if It Leads to More Wisdom. Available online: https:\/\/www.instrumentation.co.za\/8423a."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.compchemeng.2012.06.037","article-title":"Smart manufacturing, manufacturing intelligence and demand-dynamic performance","volume":"47","author":"Davis","year":"2012","journal-title":"Comput. Chem. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.procir.2014.02.001","article-title":"Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment","volume":"16","author":"Lee","year":"2014","journal-title":"Procedia CIRP"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lelli, F. (2019). Interoperability of the Time of Industry 4.0 and the Internet of Things. Future Internet, 11.","DOI":"10.3390\/fi11020036"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.procir.2016.07.038","article-title":"Industrial Big Data as a Result of IoT Adoption in Manufacturing","volume":"55","author":"Mourtzis","year":"2016","journal-title":"Procedia CIRP"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/J.ENG.2017.05.015","article-title":"Intelligent Manufacturing in the Context of Industry 4.0: A Review","volume":"3","author":"Zhong","year":"2017","journal-title":"Engineering"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Miraz, M., Ali, M., Excell, P., and Picking, R. (2018). Internet of Nano-Things, Things and Everything: Future Growth Trends. Future Internet, 10.","DOI":"10.3390\/fi10080068"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Badii, C., Bellini, P., Difino, A., and Nesi, P. (2018). Sii-Mobility: An IoT\/IoE Architecture to Enhance Smart City Mobility and Transportation Services. Sensors, 19.","DOI":"10.3390\/s19010001"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.comnet.2017.06.013","article-title":"The role of big data analytics in Internet of Things","volume":"129","author":"Ahmed","year":"2017","journal-title":"Comput. Netw."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gokalp, M.O., Kayabay, K., Akyol, M.A., Eren, P.E., and Kocyigit, A. (2016, January 15\u201317). Big Data for Industry 4.0: A Conceptual Framework. Proceedings of the 2016 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.","DOI":"10.1109\/CSCI.2016.0088"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mavr\u00edk, V., Lastra, J.L.M., and Skobelev, P. (2013). Big Data Challenges in Industrial Automation. Industrial Applications of Holonic and Multi-Agent Systems, Springer.","DOI":"10.1007\/978-3-642-40090-2"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1109\/TII.2017.2755398","article-title":"An Efficient Industrial Big-Data Engine","volume":"14","year":"2018","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.mfglet.2014.12.001","article-title":"A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems","volume":"3","author":"Lee","year":"2015","journal-title":"Manuf. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1109\/TII.2017.2670505","article-title":"A Manufacturing Big Data Solution for Active Preventive Maintenance","volume":"13","author":"Wan","year":"2017","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_16","unstructured":"National Academies of Sciences, Engineering and Medicine (2017). Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions, National Academies Press."},{"key":"ref_17","unstructured":"Bryant, R.E., Katz, R.H., and Lazowska, E.D. (2019, May 03). Big-Data Computing: Creating Revolutionary Breakthroughs in Commerce, Science, and Society. Available online: https:\/\/cra.org\/ccc\/wp-content\/uploads\/sites\/2\/2015\/05\/Big_Data.pdf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.ins.2014.01.015","article-title":"Data-intensive applications, challenges, techniques and technologies: A survey on Big Data","volume":"275","author":"Zhang","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"69","DOI":"10.2498\/cit.1002381","article-title":"A review on modern distributed computing paradigms: Cloud computing, jungle computing and fog computing","volume":"22","author":"Hajibaba","year":"2014","journal-title":"J. Compu. Inf. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Reis, M., and Gins, G. (2017). Industrial Process Monitoring in the Big Data\/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis. Processes, 5.","DOI":"10.3390\/pr5030035"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Colombo, A.W., Bangemann, T., Karnouskos, S., Delsing, J., Stluka, P., Harrison, R., Jammes, F., and Lastra, J.L. (2014). Industrial Cloud-Based Cyber-Physical Systems, Springer International Publishing. Vol. 9783319056.","DOI":"10.1007\/978-3-319-05624-1"},{"key":"ref_22","first-page":"101","article-title":"Alarm Systems Greatly Affect Offshore Facilities Amid High Oil Prices","volume":"227","author":"Habibi","year":"2006","journal-title":"World Oil Mag."},{"key":"ref_23","unstructured":"ANSI\/ISA (2019, May 03). Management of Alarm Systems for the Process Industries. Available online: https:\/\/www.isa.org\/store\/ansi\/isa-182-2016\/management-of-alarm-systems-for-the-process-industries\/46962105."},{"key":"ref_24","unstructured":"The International Association of Oil & Gas Producers (2019, May 03). Process Safety\u2014Recommended Practice on Key Performance Indicators. Available online: https:\/\/www.iogp.org\/bookstore\/product\/process-safety-recommended-practice-on-key-performance-indicators\/."},{"key":"ref_25","unstructured":"Rothenberg, D.H. (2009). Alarm Management for Process Control: A Best-practice Guide for Design, Implementation, and Use of Industrial Alarm Systems, Momentum Press. [1st ed.]."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"da Silva, M.J., Pereira, C.E., and G\u00f6tz, M. (2016, January 6\u20138). A Dynamic Approach for Industrial Alarm Systems. Proceedings of the 2016 International Conference on Computer, Information and Telecommunication Systems (CITS), Kunming, China.","DOI":"10.1109\/CITS.2016.7546404"},{"key":"ref_27","unstructured":"EEMUA (2007). Alarm Systems: A Guide to Design, Management and Procurement, EEMUA (Engineering Equipment & Materials Users Association). [2nd ed.]."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1109\/TASE.2015.2464234","article-title":"An Overview of Industrial Alarm Systems: Main Causes for Alarm Overloading, Research Status, and Open Problems","volume":"13","author":"Wang","year":"2016","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_29","unstructured":"The International Association of Oil & Gas Producers (2019, May 03). Asset integrity\u2014the key to managing major incident risks. Available online: https:\/\/www.iogp.org\/bookstore\/product\/asset-integrity-the-key-to-managing-major-incident-risks\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1214\/10-STS330","article-title":"To Explain or to Predict?","volume":"25","author":"Shmueli","year":"2010","journal-title":"Stat. Sci."},{"key":"ref_31","unstructured":"Jones, L.V. (1987). The Collected Works of John W. Tukey: Philosophy and Principles of Data Analysis 1965\u20131986, Taylor & Francis."},{"key":"ref_32","unstructured":"Mao, Y. (2015). Data Visualization in Exploratory Data Analysis: An Overview of Methods and Technologies. [Master\u2019s Thesis, University of Texas at Arlington]."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Diggle, P.J., and Chetwynd, A.G. (2011). Statistics and Scientific Method, Oxford University Press.","DOI":"10.1093\/acprof:oso\/9780199543182.001.0001"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1002\/cem.1180070502","article-title":"Multivariate exploratory data analysis and graphics: A tutorial","volume":"7","author":"Weihs","year":"1993","journal-title":"J. Chemom."},{"key":"ref_35","unstructured":"Croarkin, C., and Tobias, P. (2019, May 03). NIST\/SEMATECH e-Handbook of Statistical Methods, Available online: http:\/\/www.itl.nist.gov\/div898\/handbook."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"547","DOI":"10.21105\/joss.00547","article-title":"Missingno: a missing data visualization suite","volume":"3","author":"Bilogur","year":"2018","journal-title":"J. Open Source Softw."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Agresti, A. (2002). Categorical Data Analysis, Wiley. [2nd ed.].","DOI":"10.1002\/0471249688"},{"key":"ref_38","first-page":"1","article-title":"Imputation of categorical variables with PROC MI","volume":"113","author":"Allison","year":"2005","journal-title":"SUGI 30 Proc."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Agresti, A. (2007). An Introduction to Categorical Data Analysis, John Wiley & Sons.","DOI":"10.1002\/0470114754"},{"key":"ref_40","unstructured":"SAS Institute Inc. (2019, May 03). Introduction to Categorical Data Analysis Procedures. Available online: https:\/\/support.sas.com\/documentation\/onlinedoc\/stat\/141\/introcat.pdf."},{"key":"ref_41","first-page":"498","article-title":"Visualizing Categorical Data","volume":"43","author":"Friendly","year":"2012","journal-title":"Technometrics"},{"key":"ref_42","first-page":"1","article-title":"Pandas: a Foundational Python Library for Data Analysis and Statistics","volume":"14","author":"Mckinney","year":"2011","journal-title":"Python High Perform. Sci. Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1080\/00031305.1984.10482869","article-title":"A Mosaic of Television Ratings","volume":"38","author":"Hartigan","year":"1984","journal-title":"Am. Stat."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0008694","article-title":"Mapping Change in Large Networks","volume":"5","author":"Rosvall","year":"2010","journal-title":"PLOS ONE"},{"key":"ref_46","first-page":"37","article-title":"Working with categorical data with R and the vcd and vcdExtra packages","volume":"171","author":"Friendly","year":"2016","journal-title":"CRAN Repository"},{"key":"ref_47","unstructured":"Brunson, J.C. (2019, May 03). Available online: https:\/\/cran.r-project.org\/web\/packages\/ggalluvial\/index.html."},{"key":"ref_48","unstructured":"Bojanowski, M., and Edwards, R. (2019, May 03). Available online: https:\/\/cran.r-project.org\/web\/packages\/alluvial\/index.html."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1006\/ijhc.2000.0420","article-title":"Evaluation of space-filling information visualizations for depicting hierarchical structures","volume":"53","author":"Stasko","year":"2000","journal-title":"Int. J. Human Comput. Stud."},{"key":"ref_50","unstructured":"Russell, M.K., and True, L. (2019, May 03). Available online: https:\/\/cran.r-project.org\/web\/packages\/sunburstR\/index.html."},{"key":"ref_51","unstructured":"Mueller, A. (2019, May 03). A little word cloud generator in Python. Available online: https:\/\/github.com\/amueller\/word_cloud."},{"key":"ref_52","unstructured":"Kassambara, A. (2017). Practical Guide To Principal Component Methods in R: PCA, M(CA), FAMD, MFA, HCPC, factoextra, Multivariate Analysis, CreateSpace Independent Publishing Platform."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Husson, F., L\u00ea, S., and Pag\u00e8s, J. (2011). Exploratory Multivariate Analysis by Example Using R, CRC Press. [2nd ed.].","DOI":"10.1201\/b10345"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1007\/s11135-015-0206-0","article-title":"Multiple correspondence analysis: One only or several techniques?","volume":"50","year":"2016","journal-title":"Qual. Quant."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Rodriguez-Sabate, C., Morales, I., Sanchez, A., and Rodriguez, M. (2017). The multiple correspondence analysis method and brain functional connectivity: Its application to the study of the non-linear relationships of motor cortex and basal ganglia. Front. Neurosci., 11.","DOI":"10.3389\/fnins.2017.00345"},{"key":"ref_56","unstructured":"Husson, F., Josse, J., Le, S., and Mazet, J. (2019, May 03). Available online: https:\/\/cran.r-project.org\/web\/packages\/FactoMineR\/index.html."},{"key":"ref_57","unstructured":"Kassambara, A., and Mundt, F. (2019, May 03). Available online: https:\/\/cran.r-project.org\/web\/packages\/factoextra\/index.html."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1590\/S0104-66322002000200008","article-title":"Planning and scheduling for petroleum refineries using mathematical programming","volume":"19","author":"Joly","year":"2002","journal-title":"Braz. J. Chem. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1205\/026387603765173691","article-title":"Mixed-Integer Programming Techniques for the Scheduling of Fuel Oil and Asphalt Production","volume":"81","author":"Joly","year":"2003","journal-title":"Chem. Eng. Res. Des."},{"key":"ref_60","unstructured":"Wang, J., Zhang, W., Shi, Y., Duan, S., and Liu, J. (2018). Industrial Big Data Analytics: Challenges, Methodologies, and Applications. CoRR."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/12\/2772\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:59:55Z","timestamp":1760187595000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/12\/2772"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,20]]},"references-count":60,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["s19122772"],"URL":"https:\/\/doi.org\/10.3390\/s19122772","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,20]]}}}