The Boeing Company (20240303481). METHOD AND SYSTEM FOR PREDICTING FUEL CONSUMPTION FOR A FLIGHT OF AN AIRCRAFT simplified abstract
Contents
METHOD AND SYSTEM FOR PREDICTING FUEL CONSUMPTION FOR A FLIGHT OF AN AIRCRAFT
Organization Name
Inventor(s)
Chaitanya Pavan Kumar Aripirala of Bangalore (IN)
Veeresh Kumar Masaru Narasimhulu of Bangalore (IN)
Satyendra Yadav of Lone Tree CO (US)
METHOD AND SYSTEM FOR PREDICTING FUEL CONSUMPTION FOR A FLIGHT OF AN AIRCRAFT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240303481 titled 'METHOD AND SYSTEM FOR PREDICTING FUEL CONSUMPTION FOR A FLIGHT OF AN AIRCRAFT
Simplified Explanation: The patent application describes a method and system for predicting fuel consumption of an aircraft using a centralized machine learning model that is trained with both public and proprietary data from aircraft owners.
- The centralized machine learning model is built and trained using public data.
- A version of the model is sent to edge computing devices owned by aircraft entities.
- Aircraft owners input their proprietary data into the model received on the edge devices.
- The model is trained with this proprietary data to improve fuel consumption predictions.
- A neural network gain is determined based on a weighted average of gains from edge devices.
- This gain is used to further train the centralized model for more accurate predictions without directly using proprietary data.
Key Features and Innovation:
- Utilizes a centralized machine learning model for predicting fuel consumption.
- Incorporates proprietary data from aircraft owners through edge computing devices.
- Determines a neural network gain to enhance the accuracy of predictions.
- Enables training of the centralized model without directly accessing proprietary data.
Potential Applications: This technology can be applied in the aviation industry for optimizing fuel consumption in aircraft operations.
Problems Solved:
- Enhances the accuracy of fuel consumption predictions.
- Allows aircraft owners to contribute their proprietary data without compromising confidentiality.
Benefits:
- Improved fuel efficiency in aircraft operations.
- Enhanced predictive capabilities for fuel consumption.
- Maintains data privacy and confidentiality of proprietary information.
Commercial Applications: Fuel optimization software for airlines and aircraft operators to reduce costs and improve operational efficiency.
Prior Art: Readers can explore prior research on machine learning models for predicting fuel consumption in the aviation industry.
Frequently Updated Research: Stay informed about advancements in machine learning algorithms for fuel consumption prediction in aircraft operations.
Questions about Aircraft Fuel Consumption Prediction: 1. How does the technology ensure data privacy while incorporating proprietary information from aircraft owners? 2. What are the potential cost savings for airlines and aircraft operators by using this predictive fuel consumption technology?
Original Abstract Submitted
a method and system for predicting a fuel consumption of an aircraft is disclosed. first, a centralized machine learning model is built and trained using available public data. a version of the centralized model is then sent to one or more edge computing devices controlled by entities that own aircraft, and they input their proprietary data into the version of the centralized model they receive. the received version of the centralized model is trained using the proprietary data and a neural network gain for the centralized machine learning model is determined based on a weighted average of edge neural network gains from the edge computing devices. this neural network gain is used to further train the centralized model to give a more accurate prediction of the fuel consumption without the centralized machine learning model actually ever receiving and being trained with the proprietary data of the airlines and other entities.
