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

Aguinaldo Bezerra1, Ivanovitch Silva2, Luiz Affonso Guedes3

  • 1Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil. aguinaldo@ufrn.edu.br.

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Summary
This summary is machine-generated.

Exploratory Data Analysis (EDA) unlocks hidden knowledge in industrial alarm and event logs. This data-driven approach enhances industrial perception by extracting valuable insights from operational data without prior assumptions.

Keywords:
alarm and event managementdata scienceexploratory data analysisindustry 4.0monitoring

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Area of Science:

  • Industrial Data Science
  • Big Data Analytics
  • Process Monitoring

Background:

  • Industrial alarm and event logs are underutilized data sources.
  • Industry 4.0 and Industrial Internet of Things (IIoT) demand advanced data analysis.
  • Traditional methods fail to extract maximum value from industrial data.

Purpose of the Study:

  • To propose Exploratory Data Analysis (EDA) as a method for industrial alarm and event analysis.
  • To demonstrate EDA's capability in extracting hidden information from industrial data.
  • To enhance industrial perception through data-driven insights.

Main Methods:

  • Application of Exploratory Data Analysis (EDA) techniques.
  • Analysis of real-world industrial alarm and event log data.
  • Data-driven approach without pre-existing assumptions.

Main Results:

  • EDA effectively extracts valuable insights from industrial operational data.
  • Increased industrial perception is achieved through the analysis.
  • Latent knowledge within alarm and event logs is uncovered.

Conclusions:

  • Exploratory Data Analysis (EDA) is a powerful tool for industrial data analysis.
  • EDA enables a deeper understanding of industrial processes through log data.
  • This approach offers a promising path for leveraging industrial big data.