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Area of Science:

  • Data Science
  • Natural Language Processing
  • Machine Learning

Background:

  • Manual analysis of over 54,000 NASA space hardware failure reports is impractical for identifying trends.
  • Expert knowledge is often unavailable or unstructured for large datasets.

Purpose of the Study:

  • To develop an automated taxonomical model for text mining and knowledge extraction from engineering failure reports.
  • To structure unstructured text data for trend identification and root cause analysis.

Main Methods:

  • Utilized Latent Dirichlet Allocation (LDA) algorithms combined with Bidirectional Encoder Representations from Transformers (BERT).
  • Developed an automated taxonomical model to structure text content.

Main Results:

  • The combined LDA and BERT model provides structure to text content for analysis.
  • Enables knowledge extraction and trend identification from large datasets.

Conclusions:

  • Automated taxonomical models using LDA and BERT can overcome limitations of manual analysis and expert dependency.
  • Facilitates efficient analysis of extensive engineering failure report datasets.