Aircraft Onboard Failure Diagnosis With Causal Maintenance Reasoning
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Solution Overview
Problem
Current aircraft maintenance systems require manual interpretation of complex data to diagnose failures, leading to inefficiencies and prolonged downtime, which is costly for vehicle owners.
Innovation Solution
Implementing an onboard reasoner using a graph-theoretic algorithm and diagnostic causal model to automatically diagnose failure modes and determine maintenance actions, with an off-board reasoner for closed-loop model maturation to update and improve the onboard system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data interpretation is used for failure diagnosis, then the system can identify fault modes, but the diagnosis time and aircraft downtime increase
Solution Approach 1:
The diagnostic system performs self-diagnosis automatically without requiring manual intervention. The processor autonomously receives test results, compares them against the causal model, identifies failure modes, and generates maintenance recommendations, enabling the system to service itself diagnostically and eliminate human interpretation delays
Solution Approach 2:
The patent replaces the manual mechanical process of human data interpretation with an automated electronic system. The processor electronically compares test results with the causal model database, automatically identifies failures, and generates maintenance actions, substituting human cognitive processing with machine-based algorithmic processing to reduce diagnosis time
2Reliability
If comprehensive test data is collected from all aircraft systems, then complete failure mode identification is achieved, but the complexity of data processing increases
Solution Approach 1:
The causal model serves as an intermediary structure between raw test data and failure identification. The model organizes relationships between tests and failure modes in a structured format, allowing the processor to efficiently navigate complex data relationships without requiring complex processing algorithms, thus simplifying the data processing pathway
Solution Approach 2:
The diagnostic system segments the complex data processing task into distinct modules: test result reception, causal model comparison, failure mode identification, and maintenance recommendation generation. The causal model itself is segmented into multiple interconnected graphs representing different aircraft systems, allowing parallel processing and reducing overall system complexity
3Productivity
If automated diagnostic systems are implemented, then diagnosis speed increases, but the system complexity and development cost increase
Solution Approach 1:
The causal model serves multiple functions simultaneously: it stores knowledge about system-failure relationships, enables automated diagnosis, provides a framework for maintaining diagnostic accuracy through updates, and supports generation of maintenance recommendations. This multi-functionality reduces the need for separate systems and reduces overall complexity despite the automation benefits
Data Source
AI summary
A method is provided for diagnosing a failure on an aircraft that includes aircraft systems configured to report faults to an onboard reasoner. The method includes receiving a fault report at an onboard computer of the aircraft from an aircraft system of the aircraft systems, the fault report indicating failed tests reported by the aircraft system. The onboard reasoner accesses an onboard diagnostic causal model represented by a graph describing known causal relationships between possible failed tests reported by the respective ones of the aircraft systems, and possible failure modes of the respective ones of the aircraft systems. The onboard reasoner diagnoses a failure mode of the aircraft system or another of the aircraft systems, from the failed tests, and using a graph-theoretic algorithm and the onboard diagnostic causal model. A maintenance action is determined for the failure mode, and a maintenance message is generated including the maintenance action.