Abstract
NASA Johnson Space Center has collected more than 54,000 space hardware failure reports. Obtaining engineering processes trends or root cause analysis by manual inspection is impractical. Fortunately, novel data science tools in Machine Learning and Natural Language Processing (NLP) can be utilized to perform text mining and knowledge extraction. In NLP the use of taxonomies (classification trees) are key to the structuring of text data, extracting knowledge and important concepts from documents, and facilitating the identification of correlations and trends within the data set. Usually, these taxonomies and text structures live in the heads of experts in their specific field. However, when an expert is not available, taxonomies and ontologies are not found in data bases, or the field of study is too broad, this approach can enable and provide structure to the text content of a record set. In this paper an automated taxonomical model is presented by the combination of Latent Dirichlet Allocation (LDA) algorithms and Bidirectional Encoder Representations from Transformers (BERT). Additionally, the limitations and outcomes of causal relationship rule mining models, commercial tools, and deep neural networks are also discussed.
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Data availability
The datasets generated and/or analysed during the current study are not publicly available due to containing sensitive documents with NASA’s engineering processes information but are available from the corresponding author on reasonable request.
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Acknowledgements
The authors declare the work conducted on this project was in support of NASA-internal business practices to understand the effectiveness of standard flight hardware processes. Special thanks to the Langley Research Center Data Science Team: Charles A. Liles GCP for guidance and Jam Session organization. Theodore D. Sidehamer for IBM Watson Explorer support, demo and access. Ilangovan, Hari S. for NLP INDRA-EIDOS discussions and resources. Thanks to the Johnson Space Center: (SA) Ram Pisipati, Robert J. Reynolds for early NLP guidance. (EA IT team) Jacci Bloom, Remyi Cole, Michael Patterson, Jeffrey Myerson for providing software access and troubleshooting support. (EX Intern) Ortiz Martes, Dianeliz for giving Power BI tutorials. (EX Interns) Heriberto Triana, Emanuel Sanchez, Jacquelyne Black, Nathan Berg, Sarah Smith, Rishi K. Chitturi, (GSFC Intern) Alexandra Carpenter, and others for helping me to navigate me through my NASA experience. David Kelldorf, Martin Garcia for early GCP discussions. (Intern Coordinators) Hiba Akram, Jennifer Becerra, Annalise Giuliani, Rosie Patterson. Additional thanks to the Marshall Space Flight Center. Trevor Gevers, Micheal Steele, Adam Gorski, James Lane, Frank S. King III for AWS Comprehend guidance and access. Also thanks to Ames Research Center/Arizona State Arizona State University: Dr. Yongming Liu, Dr. Yan, and Xinyu Zhao for providing useful resources to study BERT. Thanks to David C. Smith, Samantha N. Bianco, Aref F. Malek for LDA-BERT improvement suggestions from NASA community GCP AI ML agency presentation. Finally, thanks to the Goddard Space Flight Center, NASA Center for Climate Simulation support: Ellen M. Salmon, Mark L. Carroll for granting a Virtual Machine with Linux environment to test models.
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NASA’s Office of STEM Engagement The Minority University Research and Education Project (MUREP).
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T.H. - Project Conceptualization, Data Curation, Funding, Supervision, Project Administration, General Resources. D.P. Project Formulation, Formal Analysis, Investigation, Methodology, Visualizations and Writing.
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Palacios, D., Hill, T.R. Taxonomical modeling and classification in space hardware failure reporting. Sci Rep (2026). https://doi.org/10.1038/s41598-026-36813-7
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DOI: https://doi.org/10.1038/s41598-026-36813-7
