Detection of Typical Progress Patterns of Industrial Incidents by Text Mining Technique | SpringerLink
Skip to main content
Springer Nature Link
Log in

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

  • Conference paper
  • First Online:
Advances in Human Error, Reliability, Resilience, and Performance (AHFE 2017)

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

Included in the following conference series:

  • 1557 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 17159
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 21449
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

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

    Google Scholar 

  2. International Electrotechnical Commission: IEC 61508-7:2000 Functional Safety of Electrical Electronic/Programmable Electronic Safety-Related Systems — Part 7, Overview of Techniques and Measures (2000)

    Google Scholar 

  3. Pearl, J.: Causality: Models, Reasoning and Inference. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  4. Nakata, T.: Text-mining on incident reports to find knowledge on industrial safety. In: IEEE Annual Reliability and Maintainability Symposium (2017)

    Google Scholar 

Download references

Acknowledgements

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

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Sohrab
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Toru Nakata .

Editor information

Editors and Affiliations

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

    Ronald Laurids Boring

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60644-6

  • Online ISBN: 978-3-319-60645-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation