Component Lifing via Regional Risk Exchange Rates
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Solution Overview
Problem
Current methods for determining the operational lifetime of engine components are inaccurate and time-consuming, as they rely on theoretical certification missions that may differ significantly from actual operating conditions, leading to premature failure or unnecessary replacements.
Innovation Solution
A computer-based method that divides components into regions, calculates cumulative risks, and applies exchange rates to determine the modified risk of failure based on actual or virtual missions, allowing for quick and accurate lifing by comparing the life of actual or virtual missions to a predetermined standard mission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If finite element analysis (FEA) is performed for theoretical certification missions to determine component lifetime, then the approved lifetime can be calculated with sufficient accuracy, but the process becomes too time-consuming to be used with actual in-service data
Solution Approach 1:
The patent segments the complex FEA process into two distinct phases: (1) a certification phase where a full FEA model is performed once to establish baseline lifetime predictions for theoretical missions, and (2) an operational phase where pre-calculated influence matrices and exchange rates are used to rapidly assess actual component lifetimes based on real flight data. This segmentation allows the time-consuming accurate analysis to be performed only once, while subsequent assessments use lightweight computational methods.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating influence matrices, exchange rates, and mission equivalence factors during the certification phase before the component enters service. These pre-computed parameters are stored and reused throughout the component's operational life, eliminating the need to perform full FEA analyses for each actual mission profile. The exchange rates, which quantify the severity of actual missions relative to certification missions, are determined in advance and applied directly to flight data.
2Ease of manufacture
If theoretical certification missions are used to determine component lifetime, then the approval process can be completed, but the predicted lifetime may differ significantly from actual component performance due to differences between theoretical and actual operating conditions
Solution Approach 1:
The patent implements feedback by continuously comparing actual component lifetime data with predictions from the certification model. Exchange rates are calculated based on the ratio of actual to certification mission parameters, providing feedback on how real operating conditions differ from theoretical assumptions. This feedback loop allows operators to adjust maintenance schedules and replace components based on actual performance rather than conservative theoretical estimates, improving both safety and operational efficiency.
Solution Approach 2:
The patent changes key parameters by introducing exchange rates that quantify the severity of actual missions relative to certification missions. Instead of using fixed theoretical mission parameters, the system dynamically adjusts lifetime predictions based on actual flight data parameters such as takeoff weight, altitude, temperature, and mission duration. The exchange rate serves as a scaling factor that transforms certification-based lifetime estimates into accurate predictions for actual operating conditions.
Data Source
AI summary
A method of lifing a mechanical component is provided. The method includes: (a) providing a model of the component in which the component is divided into a plurality of regions, and (b) performing consecutively, for each of a consecutive series of actual or virtual missions, the sub-steps of: (b-i) determining the number of operational cycles in the most recent actual or virtual mission for each region, (b-ii) determining a respective unmodified additional cumulative risk associated with each region, (b-iii) determining a respective modified additional cumulative risk associated with each region, (b-iv) determining an updated total modified cumulative risk associated with each region, and (b-v) summing the updated total modified cumulative risks of the regions to determine a modified risk of failure of the component.