Remote Data Management for Aircraft Diagnostic Systems
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Solution Overview
Problem
Modern aircraft systems face challenges in managing vast amounts of diagnostic data, where only a subset is of interest, and wholesale modification of diagnostic software is not practical due to certification considerations in safety-critical applications.
Innovation Solution
A computer-implemented method for managing data in remote systems, involving the reception and wireless transmission of fundamental data to a remote support center, where requests for augmented data or diagnostic protocol changes can be sent, allowing modifications to the diagnostic protocol, such as stack/table logic, sample rate, or sample time length, without affecting certification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all diagnostic data is captured and stored locally, then complete diagnostic information is available, but storage capacity is exceeded and communication bandwidth is overwhelmed
Solution Approach 1:
The system extracts only the most relevant diagnostic parameters from the vast amount of captured data based on priority levels and operational context. Critical safety parameters are identified and transmitted preferentially, while less important data is filtered out or aggregated, thereby reducing the data volume that needs to be stored and communicated while maintaining essential diagnostic completeness.
Solution Approach 2:
Different data processing strategies are applied to different types of diagnostic data based on their importance and characteristics. Safety-critical parameters receive higher priority for transmission and storage, while non-critical data is processed differently. This selective approach ensures that limited storage and communication resources are allocated efficiently to maintain the most important diagnostic information.
2Adaptability or versatility
If diagnostic software is modified to capture more relevant data, then diagnostic capability is improved, but certification requirements prevent wholesale modification
Solution Approach 1:
The diagnostic system is segmented into multiple independent layers: certified core functionality that maintains certification compliance, and configurable parameter layers that can be adjusted for different diagnostic needs. This segmentation allows the system to maintain its certified status while enabling flexible adaptation of diagnostic parameters, priorities, and protocols through configuration rather than code modification.
Solution Approach 2:
Instead of modifying the certified diagnostic software code, the system changes operational parameters such as data sampling rates, transmission priorities, filtering thresholds, and protocol selections. These parameter changes allow the system to adapt to different diagnostic requirements and operational contexts while maintaining the integrity of the certified software foundation.
3Speed
If data transmission frequency is increased, then real-time monitoring is improved, but communication bandwidth is consumed
Solution Approach 1:
The system implements periodic data transmission with variable intervals based on operational conditions and data priority. Critical safety parameters are transmitted more frequently using shorter intervals, while non-critical data uses longer intervals. This periodic action with adaptive timing maintains real-time monitoring capability for important parameters while significantly reducing overall communication bandwidth consumption compared to continuous high-frequency transmission of all data.
4Productivity
If processing power is increased to handle more data, then diagnostic analysis is improved, but onboard computing resources are limited
Solution Approach 1:
The system introduces an intermediary data processing layer that performs initial filtering, aggregation, and prioritization of diagnostic data before it reaches the onboard processing systems. This intermediary layer reduces the volume and complexity of data that requires intensive onboard processing, enabling effective diagnostic analysis within limited computational resources by handling preliminary data preparation tasks.
Data Source
AI summary
A computer implemented method for managing data of a remote system includes receiving, at a processor on the remote system, fundamental data from a monitored subsystem of the remote system and/or at least one associated subsystem of the remote system as specified by a diagnostic protocol for the monitored subsystem and wirelessly transmitting the fundamental data to a remote support center that is separate from the remote system. The remote system applies additional, non-certified, logic to determine if augmented diagnostic data is desired. The method also includes wirelessly receiving a signal from the remote support center including at least one of a request for augmented data or a diagnostic protocol change command. The method also includes receiving the augmented data from the monitored subsystem and/or the at least one associated subsystem and/or at least one other subsystem of the remote system, and transmitting the augmented data to the remote support center.