Aviation Engine Component Selection via Statistical Variation
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Solution Overview
Problem
Current methods for selecting components for aviation engines fail to introduce sufficient variation, leading to unforeseen downstream effects and premature degradation, resulting in unscheduled downtime and inefficient maintenance.
Innovation Solution
A system that calculates standard deviation based on manufacturing characteristics using a statistical distribution to select and trade components, introducing variation while maximizing durability performance and predicting maintenance schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If components are selected based on minimum manufacturing specifications only, then manufacturing precision requirements are met, but insufficient variation is introduced leading to premature degradation and unscheduled downtime
Solution Approach 1:
The patent changes the selection parameter from minimum specification compliance to statistical distribution-based selection. Components are selected based on their position within a statistical distribution (e.g., standard deviations from the mean) rather than simply meeting minimum thresholds. This introduces controlled variation while maintaining reliability by ensuring all selected components remain within acceptable ranges.
Solution Approach 2:
The patent implements dynamic trading mechanisms where components can be exchanged between engine sets based on performance data and changing operational conditions. This dynamic adjustment allows the system to optimize for durability over time while maintaining appropriate variation levels, resolving the contradiction between uniformity and reliability.
2Manufacturing precision
If components are made too uniform through strict selection, then manufacturing precision is improved, but unforeseen downstream effects occur and degradation accelerates
Solution Approach 1:
The patent intentionally introduces controlled parameter variation by selecting components at specific positions within the statistical distribution rather than choosing only the most uniform components. This controlled variation prevents the downstream effects and accelerated degradation that occur with excessive uniformity, while still maintaining high manufacturing precision standards.
3Reliability
If statistical distribution methods are used to select components, then variation is introduced improving durability, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical or manual component selection and trading systems with automated computational methods. The statistical distribution analysis, component scoring, and trading decisions are performed automatically using software algorithms, which reduces the operational complexity despite the sophisticated selection criteria. This allows advanced statistical methods to be applied without proportionally increasing system complexity.
4Reliability
If component trading is performed frequently to maximize durability, then reliability is improved, but loss of time due to maintenance operations increases
Solution Approach 1:
The patent implements periodic trading cycles rather than continuous trading operations. Component sets are evaluated and traded at predetermined intervals or based on accumulated performance data thresholds. This periodic approach maintains durability benefits while minimizing the frequency of maintenance operations and associated downtime.
Data Source
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AI summary
Systems, computer-implemented methods and/or computer program products that facilitate selecting components for aviation engines. In one embodiment, a computer-implemented method comprises: calculating, by a system 100 operatively coupled to a processor 106, standard deviation between components for aviation engines based on a statistical distribution of measured manufacturing characteristics; selecting, by the system 100, a subset of components based on the calculated standard deviation; and analyzing, by the system 100, the subset of components at respective standard deviation to determine a standard deviation and period of time for trading that maximizes durability performance.