Revolutionizing Space Hardware Failure Reporting with Taxonomical Modeling

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. 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. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions