Avionic Failure Rate Evaluation Method
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Solution Overview
Problem
Current methods lack the ability to objectively evaluate the improvement in failure rates of avionic units after modification or replacement, hindering the assessment of performance enhancements such as reduced failure rates.
Innovation Solution
A method and electronic device connected to multiple databases that extract and analyze serial numbers, flight data, and monitoring messages to calculate failure occurrence rates for different avionic units, allowing for the determination of a rate of change in failure rates between unit types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional databases and reporting methods are used, then basic reliability reports can be produced, but the ability to evaluate performance improvement after unit modification or replacement is lost
Solution Approach 1:
The method segments the analysis by creating separate populations (first population with first unit type, second population with second unit type) and calculating failure rates independently for each, then comparing them to evaluate performance improvement. This segmentation allows targeted evaluation of modification effects without mixing data from different unit types.
Solution Approach 2:
The system establishes a feedback loop by continuously monitoring failure messages, calculating failure rates, and comparing them across different unit types. This enables operators to evaluate whether modifications or replacements actually improved reliability, providing feedback for future maintenance decisions.
2Measurement precision
If detailed monitoring and analysis of all avionic units is performed, then accurate failure rate evaluation is achieved, but the complexity of data processing and analysis increases significantly
Solution Approach 1:
The method extracts only the necessary data elements (failure messages associated with specific unit types, flight hours, and population identifiers) from the comprehensive database, rather than processing all available data. This extraction approach maintains measurement precision while reducing processing complexity by focusing only on relevant information.
Solution Approach 2:
The system changes the parameter of analysis by grouping units into populations based on unit type identifiers and calculating aggregate failure rates rather than analyzing individual unit failures. This parameter transformation simplifies the data structure while preserving the ability to make precise comparisons between different unit types.
Data Source
AI summary
A method for evaluating a rate of change of a failure between a first unit type and a second unit type of an avionic system, the method taking into account, for each aircraft comprising the unit type, the number of flights made and the number of fault messages associated with the failure.