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Detection of Typical Progress Patterns of Industrial Incidents by Text Mining Technique

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Advances in Human Error, Reliability, Resilience, and Performance (AHFE 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 589))

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Abstract

To prevent accidents, it is very important to learn why and how past accidents occurred and escalated. The information of accidents is mostly recorded in natural language texts, which is not convenient to analyze the flow of events in the accidents. This paper proposes a method to recognize typical flow of events in a large set of text reports. By focusing two adjacent sentences, our system succeeded to detect typical pairs of predecessor word and successor word. Then we can recognize the typical flows of accidents.

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References

  1. Center for Chemical Process Safety: Guidelines for Implementing Process Safety Management, 2nd edn. (2016)

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Acknowledgements

This paper is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).

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Authors and Affiliations

  1. Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Aomi 2-4-7, Koto-ku, Tokyo, Japan

    Toru Nakata & Mohammad Sohrab

Authors
  1. Toru Nakata
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  2. Mohammad Sohrab
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Corresponding author

Correspondence to Toru Nakata .

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Editors and Affiliations

  1. Idaho National Laboratory, Idaho Falls, Idaho, USA

    Ronald Laurids Boring

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Nakata, T., Sohrab, M. (2018). Detection of Typical Progress Patterns of Industrial Incidents by Text Mining Technique. In: Boring, R. (eds) Advances in Human Error, Reliability, Resilience, and Performance. AHFE 2017. Advances in Intelligent Systems and Computing, vol 589. Springer, Cham. https://doi.org/10.1007/978-3-319-60645-3_22

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  • DOI: https://doi.org/10.1007/978-3-319-60645-3_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60644-6

  • Online ISBN: 978-3-319-60645-3

  • eBook Packages: EngineeringEngineering (R0)

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