Aircraft Data Retrieval System for In-Flight Abnormal Condition Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods fail to effectively detect and diagnose abnormal conditions in aircraft sub-systems during flight, making it difficult to identify the specific source of degradation or failure, especially since these conditions often persist after landing, requiring manual intervention and delaying corrective actions.
Innovation Solution
A system that uses satellite communication to request and retrieve relevant aircraft data, including parameter measurement and analysis, to automatically detect abnormal conditions and identify the source of issues, allowing for real-time monitoring and communication of health and trend data between the aircraft and ground support networks without requiring flight crew intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring and diagnosis methods are used for aircraft sub-systems, then flight crew can detect abnormal conditions, but the detection capability is insufficient and diagnosis accuracy is poor
Solution Approach 1:
The patent introduces an automated monitoring system with onboard computers and ground-based analysis systems as intermediaries between the aircraft sub-systems and flight crew. This intermediary system continuously collects, transmits, and analyzes data from aircraft sensors, enabling precise detection of abnormal conditions without requiring manual monitoring by flight crew, thus improving detection capability while maintaining operational simplicity
Solution Approach 2:
The patent replaces manual monitoring and diagnosis mechanisms with automated electronic monitoring systems. Onboard computers continuously collect data from aircraft sub-systems, transmit it via satellite communication, and ground-based systems automatically analyze the data to diagnose abnormalities. This substitution of mechanical/manual processes with electronic automation significantly enhances detection precision and diagnostic accuracy
2Reliability
If automated monitoring systems are implemented to detect abnormal conditions in real-time, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent divides the monitoring system into distinct functional segments: onboard data collection modules distributed across different aircraft sub-systems, a central onboard computer for data aggregation, satellite communication interfaces, and ground-based analysis systems. This segmentation allows each component to perform its specific function independently, improving overall detection reliability while managing system complexity through modular architecture
Solution Approach 2:
The onboard computer and ground-based systems serve multiple functions: they monitor normal operational parameters, detect abnormal conditions, diagnose specific failure sources, and provide maintenance recommendations. This multi-functionality consolidates what would otherwise require separate systems into a unified platform, enhancing detection accuracy without proportionally increasing system complexity
3Loss of time
If continuous monitoring of all aircraft parameters is performed, then abnormal conditions are detected early, but data transmission requirements and communication bandwidth increase
Solution Approach 1:
The system continuously monitors all aircraft parameters but selectively transmits only relevant data to ground systems. Normal operational data is processed locally by onboard computers, while only abnormal conditions, diagnostic information, and maintenance-critical parameters are transmitted via satellite. This partial transmission approach enables early detection of all parameters while minimizing communication bandwidth requirements
Solution Approach 2:
The patent extracts and transmits only the essential diagnostic information to ground-based systems rather than transmitting complete continuous data streams. The onboard computer identifies abnormal conditions and extracts specific parameter values, time stamps, and diagnostic codes for transmission. This extraction of critical information maintains rapid detection capability while significantly reducing the volume of data requiring satellite transmission
4Productivity
If manual intervention by flight crew is required for data collection and analysis, then system operation is simpler, but productivity and response time decrease
Solution Approach 1:
The monitoring system operates autonomously without requiring flight crew intervention. Onboard computers automatically collect data from aircraft sub-systems, ground-based systems automatically receive and analyze the transmitted data, and the system self-generates diagnostic reports and maintenance recommendations. This self-service automation dramatically increases diagnosis speed and productivity while the flight crew maintains simple oversight functions
Solution Approach 2:
The system performs preliminary data collection, processing, and analysis actions automatically before any human intervention is needed. Ground-based systems continuously receive and pre-analyze aircraft data, preparing diagnostic information in advance. When abnormalities are detected, the system has already performed initial diagnosis and is ready to provide maintenance guidance immediately, eliminating delays associated with manual data collection and analysis
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
The disclosed embodiments relate to methods and systems for requesting and retrieving aircraft data during flight of an aircraft. This aircraft data can be used to perform additional monitoring of aircraft sub-systems to detect an abnormal condition, and/or to identify one or more sources that are causing the abnormal condition. In one embodiment, aircraft data for one or more relevant parameters can be requested from the ground, measured on-board the aircraft, and stored in a data file that is then communicated back to personnel on the ground. The real-time aircraft data for one or more relevant parameters can then be analyzed to identify the one or more sources that are causing the abnormal condition.