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MWO2KG and Echidna: Constructing and exploring knowledge graphs from maintenance data

Author

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  • Michael Stewart
  • Melinda Hodkiewicz
  • Wei Liu
  • Tim French

Abstract

Unstructured technical texts are a rich resource of engineering knowledge underutilised for data analysis. Maintenance work orders (MWO), for example, capture valuable information to inform what work was done on an asset and why. Data in MWO short text fields is unstructured, terse and jargon-rich, complicating the ability of both humans and machines to read it. Our challenge is to efficiently extract technical information from the MWO short text field and combine it with data in structured fields such as dates, functional location, make and model of the asset. In this paper we present a technical language processing-based solution for this problem. Echidna is an intuitive query-enabling interface that visualises historic asset data in the form of a knowledge graph. This knowledge graph is produced by MWO2KG, which uses deep learning supported by annotated training data to automatically construct knowledge graphs from unstructured technical text combined with data from structured fields. The tools are tested on maintenance work order and delay accounting data provided by industry partners. These tools provide reliability engineers with an efficient way to find information in historic asset data for failure modes and effects analysis, maintenance strategy validation and process improvement work. Source code for both tools is available on GitHub under the Apache 2.0 License.

Suggested Citation

  • Michael Stewart & Melinda Hodkiewicz & Wei Liu & Tim French, 2024. "MWO2KG and Echidna: Constructing and exploring knowledge graphs from maintenance data," Journal of Risk and Reliability, , vol. 238(5), pages 920-932, October.
    • Handle: RePEc:sae:risrel:v:238:y:2024:i:5:p:920-932
    DOI: 10.1177/1748006X221131128
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    References listed on IDEAS

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    1. Yang, Zhe & Baraldi, Piero & Zio, Enrico, 2020. "A novel method for maintenance record clustering and its application to a case study of maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
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