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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.
Sensors (Basel, Switzerland)
|June 23, 2019
Summary
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.
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.
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