Identifying equipment health status from maintenance records using Lexicon based Unsupervised Sentiment Analysis Adjusted for Negation (LUSAA-N)

During periodic inspection and repair of industrial equipment, technicians create maintenance records in the form of unstructured text to document valuable information regarding the equipment condition, operating environment, failed components, failure mechanism, etc. Inferring the equipment health status from these records can help in planning future maintenance actions to avoid expensive machine failures. However, automatic inference of the equipment health status is non-trivial due to various challenges inherent in analyzing unstructured text such as domain-specific vocabulary and negation words. Further, manual labeling of the records for supervised classification is arduous and impractical. Thus, in this paper, we present a model to automatically extract the equipment health status from maintenance records in a completely unsupervised manner. We show that using general English sentiment lexicons is highly inefficient for analyzing maintenance records, and thus propose a method that leverages sentiment lexicons curated for the maintenance domain and addresses the central issue of negation. We demonstrate the effectiveness of our proposed model over extant research using data from real-life maintenance records from oil rig equipment.