Text Classification and Tagging of United States Army Ground Vehicle Fault Descriptions in Support of Data-Driven Prognostics

Abstract

The manner in which a prognostics problem is framed is critical for enabling its solution by the proper method. Recently, data-driven prognostics techniques have demonstrated enormous potential when used alone, or as part of a hybrid solution in conjunction with physics-based models. Historical maintenance data constitutes a critical element for the use of a data-driven approach to prognostics, such as supervised machine learning. The historical data is used to create training and testing data sets to develop the machine learning model. Categorical classes for prediction are required for machine learning methods; however, faults of interest in US Army Ground Vehicle Maintenance Records appear as natural language text descriptions rather than a finite set of discrete labels.
 Transforming linguistically complex data into a set of prognostics classes is necessary for utilizing supervised machine learning approaches for prognostics. Manually labeling fault description instances is effective, but extremely time-consuming; thus, an automated approach to labelling is preferred. The approach described in this paper examines key aspects of the fault text relevant to enabling automatic labeling. A method was developed based on the hypothesis that a given fault description could be generalized into a category. This method uses various natural language processing (NLP) techniques and a priori knowledge of ground vehicle faults to assign classes to the maintenance fault descriptions.
 The core component of the method used in this paper is a Word2Vec word-embedding model. Word embeddings are used in conjunction with a token-oriented rule-based data structure for document classification. This methodology tags text with user-provided classes using a corpus of similar text fields as its training set. With classes of faults reliably assigned to a given description, supervised machine learning with these classes can be applied using related maintenance information that preceded the fault.
 This method was developed for labeling US Army Ground Vehicle Maintenance Records, but is general enough to be applied to any natural language data sets accompanied with a priori knowledge of its contents for consistent labeling. In addition to applications in machine learning, generated labels are also conducive to general summarization and case-by-case analysis of faults. The maintenance components of interest for this current application are alternators and gaskets, with future development directed towards determining the RUL of these components based on the labeled data.

Evaluating word representations in a technical language processing pipeline

“…For word embeddings, we saw how visualizing words in a cluster can be used to evaluate how different misspelled words may appear. In fact, this insight may explain some motivation behind the work of Hansen, Coleman, Zhang, and Seale (2020), who used a word embeddings approach to assist in tagging data for document classification.…”

Evaluating Vector Representations of Short Text Data for Automating Recommendations of Maintenance Cases

“…Many approaches have utilized these in conjunction with word representation models for TLP (Nandyala, Lukens, Rathod, & Agarwal, 2021). A few examples in the maintenance and support domains include (Edwards, Zatorsky, & Nayak, 2008;Salo, McMillan, & Connor, 2019;Hansen, Coleman, Zhang, & Seale, 2020;Sexton, Brundage, Hodkiewicz, & Smoker, 2018). Approaches that utilize phrase-based TLP for various use cases have typically relied on n-gramming of text or pre-defined vocabularies of concept tags (Navinchandran, Sharp, Brundage, & Sexton, 2019;Pau, Tarquini, Iannitelli, & Allegorico, 2021;Ottermo, Håbrekke, Hauge, & Bodsberg, 2021).…”

“…In the prior art we find one of two scenarios (and in many instances, both scenarios). We find the related work is either only partially automated and thus requires manual intervention to facilitate the case clustering, such as in (Salo et al, 2019;Ottermo et al, 2021), and/or the related work is processing a significant volume of text from each case to perform the clustering, such as in (Hansen et al, 2020;Pau et al, 2021).…”