Technical language processing for Prognostics and Health Management: applying text similarity and topic modeling to maintenance work orders

Abstract

Modern manufacturing paradigms have incorporated Prognostics and Health Management (PHM) to implement data-driven methods for fault detection, failure prediction, and assessment of system health. The maintenance operation has similarly benefitted from these advancements, and predictive maintenance is now being used across the industry. Despite these developments, most of the approaches in maintenance rely on numerical data from sensors and field devices for any sort of analysis. Text data from Maintenance Work Orders (MWOs) contain some of the most crucial information pertaining to the functioning of systems and components, but are still regarded as ‘black holes’, i.e., they store valuable data without being used in decision-making. The analysis of this data can help save time and costs in maintenance. While Natural Language Processing (NLP) methods have been very successful in understanding and examining text data from non-technical sources, progress in the analysis of technical text data has been limited. Non-technical text data are usually structured and consist of standardized vocabularies allowing the use of out-of-the-box language processing methods in their analysis. On the other hand, records from MWOs are often semi-structured or unstructured; and consist of complicated terminologies, technical jargon, and industry-specific abbreviations. Deploying traditional NLP to such data can result in an imprecise and flawed analysis which can be very costly. Owing to these challenges, we propose a Technical Language Processing (TLP) framework for PHM. To illustrate its capabilities, we use text data from MWOs of aircraft to address two scenarios. First, we predict corrective actions for new maintenance problems by comparing them with existing problems using syntactic and semantic textual similarity matching and evaluate the results with cosine similarity scores. In the second scenario, we identify and extract the most dominant topics and salient terms from the data using Latent Dirichlet Allocation (LDA). Using the results, we are able to successfully link maintenance problems to standardized maintenance codes used in the aviation industry.