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Harnessing data mining to explore incident databases.

Sumit Anand1, Nir Keren, Marietta J Tretter

  • 1Mary Kay O'Connor Process Safety Center, Chemical Engineering Department, Texas A&M University, College Station, TX 77843-3122, USA.

Journal of Hazardous Materials
|November 29, 2005
PubMed
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This study used data mining on incident reports to find patterns in equipment failures and chemical releases. These findings help update equipment failure probabilities for better safety analysis.

Area of Science:

  • Environmental Science
  • Data Science
  • Risk Management

Background:

  • Numerous incident databases exist, but analysis often remains superficial.
  • Understanding incident patterns is crucial for improving safety and risk assessment.

Purpose of the Study:

  • To apply data mining techniques to incident data for pattern discovery.
  • To identify relationships between incident characteristics and equipment failures.
  • To refine annual equipment failure probabilities using identified patterns.

Main Methods:

  • Data extraction from the National Response Center database for fixed facilities in Harris County, TX.
  • Classification of incident data by equipment type, chemical released, and causes.
  • Application of data mining techniques to uncover significant patterns.

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Main Results:

  • Identified distinct patterns in incidents based on equipment, chemical, and causal factors.
  • Revealed correlations between specific incident characteristics and equipment failure modes.
  • Demonstrated the utility of pattern analysis in modifying failure probability estimations.

Conclusions:

  • Data mining of incident databases offers valuable insights beyond initial data exploration.
  • Understanding incident patterns can lead to more accurate risk assessments and improved equipment reliability.
  • The methodology provides a framework for enhancing predictive models in industrial safety.