Short name

AI Tracking System For Electrical Wiring on Single Maintenance Record

United Aircraft Technologies (UAT) and Beacon Works are teaming up to provide a solution and tool for the scattered maintenance data and procedures.

Electrical systems are great diagnostic and performance tools since it is what gives power to the entire platform. UAT has created a network of sensors that are embedded into clamps for wiring harnesses that can monitor, assess, diagnose, and collect data of the electrical system performance. Using modules and scattered data approach, UAT can use real-time and historical data to train algorithms to predict maintenance before a failure occurs. The data collected can also be used as a source of information to confirm ground truths, environmental data, and component performance.

Real-time data dissemination requires the creation of dictionaries. Beacon has classified its dictionary to allow predictive maintenance to be trained on the classification and historical aircraft effect. Beacon can execute predictive maintenance via single records to maintenance organizations from planners to mechanics. Instead of trading condition of components for the historical records and replacement, Beacon combines both to execute an AI real-time holistic picture of the aircraft in need of service. This holistic picture has been found to increase the efficiency per flight hour.

Expeditionary Technology Search (xTechSearch) Dual-Use Technologies applicable to Army modernization priority areas

United Aircraft Technologies (UAT) and Beacon Works are teaming up to provide a solution and tool for the scattered maintenance data and procedures.

Electrical systems are great diagnostic and performance tools since it is what gives power to the entire platform. UAT has created a network of sensors that are embedded into clamps for wiring harnesses that can monitor, assess, diagnose, and collect data of the electrical system performance. Using modules and scattered data approach, UAT can use real-time and historical data to train algorithms to predict maintenance before a failure occurs. The data collected can also be used as a source of information to confirm ground truths, environmental data, and component performance.

Real-time data dissemination requires the creation of dictionaries. Beacon has classified its dictionary to allow predictive maintenance to be trained on the classification and historical aircraft effect. Beacon can execute predictive maintenance via single records to maintenance organizations from planners to mechanics. Instead of trading condition of components for the historical records and replacement, Beacon combines both to execute an AI real-time holistic picture of the aircraft in need of service. This holistic picture has been found to increase the efficiency per flight hour.