Taxonomical Modeling and Classification in Space Hardware Failure Reporting

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.