Even in the increasingly connected world of smart manufacturing and Industrial Internet Of Things (IIOT), there will always be a need for human operators and evaluations. When creating equipment condition monitoring models and heuristics, the human operator observations are often difficult to quantify or track. This situation can lead to the observations being misunderstood or ignored completely. This work seeks to highlight the untapped potential for augmenting numeric data from sensors and control systems with human input and vice versa, by integrating documented natural language reports with data collection technology in a novel and intuitive way. This is an exploratory work that utilizes an experimental setup with a limited and controlled accelerated aging setup where human observations were recorded at regular intervals. Data collected helps validate the concept and begin developing the process of linking natural language to quantified sensed values. Recommendations for follow on work and extensions of the performed analysis are provided as part of a gaps and a next steps outline.
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Maintenance, Technical Language Processing, Natural Language Processing, Sensors, IOT, Manufacturing
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