Aircraft Message Monitoring Using Maintenance Correlation Filtering
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Solution Overview
Problem
Existing systems face challenges in efficiently filtering out non-mission critical aircraft operational messages from the vast amount of data transmitted, leading to time-consuming analysis and potential false correlations, which can hinder the determination of relevant messages for safe aircraft operation.
Innovation Solution
The Aircraft Message Monitor (AMM) server collates historical message traffic to determine correlations between message types and maintenance operations, filtering out non-mission critical messages by calculating confidence levels and associating message types with maintenance operations, thereby reducing noise and improving analysis accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all aircraft operational messages are analyzed, then comprehensive monitoring is achieved, but analysis time and computational resources increase significantly
Solution Approach 1:
The patent segments the aircraft message stream into different categories (maintenance messages, status messages, warning messages, etc.) and applies different analysis strategies to each segment. This allows critical messages to receive detailed analysis while non-critical messages are processed more efficiently, resolving the contradiction between comprehensive monitoring and analysis time.
Solution Approach 2:
The system performs preliminary filtering and classification of messages before detailed analysis. By pre-identifying and filtering out known non-critical message patterns using historical data and maintenance logs, the system reduces the volume of messages requiring full analysis, thus decreasing analysis time while maintaining comprehensive monitoring of critical issues.
2Measurement precision
If correlation analysis is performed on all messages, then message relevancy is improved, but false correlations increase
Solution Approach 1:
The system performs preliminary filtering using maintenance logs and historical data before conducting correlation analysis. By pre-identifying and removing messages with known causes (such as messages generated after deliberate equipment disconnection), the system reduces the input data for correlation analysis, thereby improving message relevancy detection while minimizing false correlations.
Solution Approach 2:
The patent introduces maintenance logs and historical message databases as intermediary references in the correlation analysis process. These intermediaries provide context about known maintenance actions and their associated messages, allowing the system to distinguish between messages indicating actual problems and those resulting from normal maintenance operations, thus reducing false correlations.
3Measurement precision
If detailed analysis of all messages is performed, then diagnosis accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the message analysis process into multiple stages: initial filtering, priority classification, and detailed analysis. By dividing the processing workflow and applying different levels of analysis to different message segments, the system maintains high diagnosis accuracy for critical messages while reducing overall processing complexity through hierarchical handling.
Solution Approach 2:
The system applies detailed analysis selectively to only those messages that pass through the filtering stages and are identified as potentially critical. Rather than performing exhaustive detailed analysis on all messages, the system applies partial action (detailed analysis) only where necessary, maintaining diagnosis accuracy for important cases while reducing processing complexity overall.
Data Source
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AI summary
A system (200) for monitoring aircraft operational messages is provided. The system (200) includes a computing device (210) including a processor (305) in communication with a memory (310). The computing device (210) is programmed to receive a plurality of historical messages for a plurality of aircraft (225). Each message of the plurality of historical messages is a message from one of the plurality of aircraft (225). The computing device (210) is also programmed to receive a plurality of historical maintenance operations performed on the plurality of aircraft (225), compare the plurality of historical messages to the plurality of historical maintenance operations to determine at least one message type associated with at least one maintenance operation type, and generate a plurality of message type correlations between message types and maintenance operation types based on the comparison.