Aircraft Automation Synoptic Display for CFIS Alerting
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Solution Overview
Problem
Modern airliner flight decks lack a consolidated display of automation function configurations, leading to inefficiencies in providing safety-related information to flight crews, particularly in scenarios where automation functions autonomously disengage without clear notification, hindering timely mitigation of Controlled Flight into Stall (CFIS) accidents.
Innovation Solution
Implementing a computer-implemented system using machine learning to analyze historical flight data and generate probabilistic alert rules, which provides a single synoptic display of automation function configurations, offering flight crews organized and timely information about automation function changes, including autonomous disengagements, through a graphical interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automation functions are incrementally layered to support flight crew, then automation control capability is improved, but display complexity and information organization deteriorate
Solution Approach 1:
The patent consolidates multiple distributed automation status indications into a single synoptic display that presents all automation function configurations in one organized location. This merging approach maintains comprehensive automation control capability while resolving the display complexity issue by providing a unified view rather than scattered information across multiple displays.
Solution Approach 2:
The synoptic display serves multiple functions simultaneously: it consolidates automation status information, provides contextual flight data, generates probabilistic alerts for anomalous configurations, and enables single-point interaction for information retrieval. This multi-functionality addresses the complexity issue while preserving full automation capability.
2Loss of information
If automation status is annunciated in distributed fashion across displays, then information coverage is improved, but information retrieval efficiency deteriorates
Solution Approach 1:
The patent merges all automation function configuration information into a single synoptic display, eliminating the need for flight crew to scan multiple distributed displays. This maintains complete information coverage while dramatically reducing retrieval time by presenting all relevant data in one location.
Solution Approach 2:
The system proactively generates and displays consolidated automation status information before the flight crew needs to request it, and proactively generates probabilistic alerts when anomalous configurations are detected. This preliminary presentation of information eliminates the time required for sequential information gathering across multiple displays.
3Device complexity
If existing display systems are used without historical flight data analysis, then system simplicity is maintained, but safety monitoring capability deteriorates
Solution Approach 1:
The patent implements feedback by analyzing historical flight data to establish baseline automation behavior patterns, then comparing real-time automation configurations against these patterns. When deviations are detected, the system generates probabilistic alerts. This feedback mechanism enhances safety monitoring capability while maintaining relative system simplicity through automated analysis.
Solution Approach 2:
The system performs preliminary analysis of historical flight data during ground operations to build knowledge bases of normal automation behavior. This preliminary action enables the onboard system to quickly compare current configurations against established patterns without requiring complex real-time analysis, thus enhancing safety monitoring while maintaining operational simplicity.
Data Source
AI summary
Methods and system for improved aircraft safety are described herein. A machine learning model may be trained using historical flight data for a number of previously flown flights where an automation function autonomously disengaged. The trained machine learning model may be used to generate probabilistic alert rules. The probabilistic alert rules may be used by a computing device onboard an aircraft to provide contextual information to flight crew relating to engagement status for one or more automation functions of the aircraft.