SBIR/STTR Award attributes
Information captured in Air Force maintenance records provides vital data that can be used by numerous communities in planning, logistics, and specifically, condition-based maintenance (CBM) as well as other maintenance-related disciplines. However, today’s data capture process can be labor intensive and prone to errors and it is difficult to assure a high level of data quality given the vast amount of enumerated codes, parts, actions, and maintenance types as well as the heavy demands and time constraints on Air Force maintainers. Easing the data capture process reduces the time maintainers spend performing non-maintenance actions. Mobile technologies paired with machine learning offer an excellent pathway towards simplifying data capture and improving data quality. Providing maintainers with mobile technology allows them to interact with these technologies as part of the maintenance workflow. Moreover, mobile platforms have numerous software tools resident on devices that can be utilized to improve data collection. By leveraging speech-to-text software and a machine learning pipeline, a maintainer can simply dictate narratives into a device with the software, and their notes will be transcribed, relevant data elements extracted with the resulting dataset recorded to the maintenance record.
