Northrop Grumman has patented a system for aircraft icing prediction using machine learning. The system processes real-time environmental data to predict icing risk, issue alerts, and recommend avoidance maneuvers. It includes a database of historical data and does not require onboard weather radar, making it suitable for smaller aircraft. GlobalData’s report on Northrop Grumman gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Northrop Grumman, Superconducting quantum circuits was a key innovation area identified from patents. Northrop Grumman's grant share as of February 2024 was 66%. Grant share is based on the ratio of number of grants to total number of patents.
Aircraft icing prediction system using machine learning for safety
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A recently granted patent (Publication Number: US11922331B2) discloses a system for aircraft icing prediction that includes an aircraft-mounted environmental sensor suite capable of measuring various environmental parameters external to the aircraft as it travels along a flight path. The system comprises data acquisition circuitry to acquire real-time environmental data, an onboard computer system with supervised and reinforcement learning engines to process the data and compute the probability of icing, and a database storing a library of learning about previous icing avoidance maneuvers. Based on this information, the system can issue icing risk alerts and recommend icing avoidance maneuvers to ensure aircraft safety.
The system also includes a user interface to convey generated alerts to crew members or remote operators, a confidence metric to display the predicted icing condition's confidence level, and the ability to generate recommended icing avoidance maneuvers based on real-time environmental data and other factors like airspeed, altitude, GPS data, and current weather conditions. Additionally, an onboard autonomous control system can execute recommended maneuvers or provide recommendations to the flight control computer. The method involves regression analysis of real-time environmental data using supervised learning to predict icing risk, determining confidence metrics using reinforcement learning, and issuing alerts or recommendations based on user-defined safety parameters. The system aims to enhance aircraft safety by predicting and preventing potential icing incidents based on a comprehensive analysis of environmental data and historical flight information stored in a database.
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