Flight Safety Analytics Display Using Predictive QAR Data
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Solution Overview
Problem
Aircraft crew members lack real-time awareness of safety events during flights, which can lead to inadequate decision-making in emergency situations, as existing systems fail to proactively identify and mitigate safety hazards effectively.
Innovation Solution
A system and method for calculating and displaying flight safety analytics using historical flight data from a quick access recorder (QAR), processing this data to identify safety events, analyzing statistical trends, and applying a predictive model to forecast potential safety events, which are then displayed on an onboard flight display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If historical flight data is processed and analyzed to predict safety events, then flight crew awareness and decision-making capability are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary processing of historical flight data to create a database of safety events and applies predictive models before flights occur. This allows safety analytics to be prepared in advance, enabling rapid assessment during actual flights without real-time processing delays or excessive computational complexity during critical phases.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes a flight management system and predictive modeling component. This intermediary processes historical data offline to generate safety analytics, which are then presented to flight crews in a simplified format, mediating between complex data processing and simple crew decision-making interfaces.
2Loss of time
If real-time safety event prediction is implemented, then response time to safety hazards is reduced, but computational load and data processing time increase
Solution Approach 1:
The system pre-processes historical flight data to build predictive models and safety event databases before flights. During actual flights, the system only needs to query pre-computed analytics and compare current flight parameters against predicted safety events, dramatically reducing real-time computational load while maintaining rapid response capability.
Solution Approach 2:
The system focuses computational resources on predicting only the most critical safety events based on historical patterns, rather than analyzing all possible flight parameters in real-time. This partial action approach concentrates processing power on high-value predictions, reducing overall computational burden while maintaining effective safety monitoring.
Data Source
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AI summary
Methods and systems are provided for calculating and displaying flight safety analytics for an aircraft. The method comprises first receiving historical flight data for the aircraft from a flight data quick access recorder (QAR) located onboard the aircraft. The flight data is then processed to identify safety events and store the identified safety events in an events database. The contents of the events database are analyzed to determine statistical data regarding the identified safety events. An onboard predictive model is applied that utilizes the statistical data to predict the likelihood of a safety event based on current flight data received from the aircraft. The predicted likelihood of a safety event is displayed on a flight display of the aircraft.