High-quality maintenance data is crucial for the upkeep and utilization of predictive maintenance capabilities. This will lead to better allocation of resources, and other effects that ultimately improve safety. However, the current user interface does not capture data accurately or efficiently.
Our solution leverages AR technology to track movement and location data in real-time, generating automatic reports with minimum human input. The generated reports are validated by maintainers in real-time and improve the accuracy of the future prediction using ML model. As next steps, the structured data will be used for predictive maintenance and inventory planning.
One of the biggest challenges is to build the standard library as a starting point. To do this, we plan to collect standardized data inputs for basic maintenance procedures.