Aircraft Component Remaining Life Prediction Using Precomputed Databases
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Solution Overview
Problem
Current life cycle management methods for high-value assets like aircraft face challenges in predicting damage evolution, especially with advanced materials, due to uncertainties in model inputs and the time-consuming nature of calculations, which limits their practical application and increases costs.
Innovation Solution
A decision-making approach using a precomputed database generated by a damage evolution model, calibrated with empirical information, that employs multivariate inverse methods to estimate future system behavior and identify root causes, incorporating eddy current sensors for data collection and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional damage evolution models are used for life cycle management predictions, then prediction accuracy is improved, but calculation time becomes extremely time consuming
Solution Approach 1:
The patent pre-calculates damage evolution predictions for a comprehensive range of possible input conditions (crack sizes, material properties, loading scenarios) and stores these results in a database before actual service use. During operation, the system quickly retrieves and interpolates from this pre-computed database rather than performing time-consuming calculations in real-time, thus achieving both high prediction accuracy and rapid response for interactive decision-making
Solution Approach 2:
The patent creates a simplified computational copy of the complex damage evolution model by storing pre-computed results in a database that mimics the behavior of the full model. This database copy allows rapid querying and interpolation to predict damage evolution for specific conditions without executing the computationally intensive original model, effectively replacing real-time calculation with fast data retrieval
2Reliability
If NDE inspection intervals are set frequently to detect rapid crack growth, then detection reliability is improved, but aircraft availability and costs increase
Solution Approach 1:
The patent transitions from static, fixed inspection intervals to dynamic, adaptive inspection scheduling. The system continuously updates crack growth predictions based on actual service conditions and retrieved database results, then dynamically adjusts inspection timing to match the actual damage evolution rate. This allows extending intervals when growth is slow while maintaining reliable detection when growth accelerates, optimizing both safety and aircraft availability
Solution Approach 2:
The patent changes the inspection interval parameter dynamically based on predicted crack growth rates derived from the database system. Instead of using fixed intervals, the system calculates optimal intervals by retrieving pre-computed damage evolution data for current crack sizes and service conditions, then adjusts inspection scheduling to maintain detection reliability while minimizing aircraft downtime and costs
3Reliability
If damage tolerance methods assume initial crack sizes below detection threshold, then safety margins are improved, but inspection intervals become more conservative and costly
Solution Approach 1:
The patent replaces the mechanical/manual process of setting conservative inspection intervals with an automated computational system. The database contains pre-calculated crack growth predictions that the system automatically queries to determine optimal inspection intervals, eliminating the need for manual conservative estimates and reducing scheduling complexity while maintaining safety margins
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables rapid and accurate predictions of remaining life and root cause identification, reducing costs and improving operational readiness by leveraging sparse data and nonlinear modeling, thus enhancing life cycle management efficiency.
Implementation Method 1
A common part of the life cycle management methods is the use of Nondestructive evaluation (NDE) methods. NDE methods provide information about near-surface, and bulk material condition for flat and curved parts or components.
Implementation Method 2
These sensors permit characterization of bulk and surface material conditions. Characterization of bulk material condition includes (1) measurement of changes in material state, i.e., degradation/damage caused by fatigue damage, creep damage, thermal exposure, or plastic deformation
Data Source
AI summary
Predicting the remaining life of individual aircraft, fleets of aircraft, aircraft components and subpopulations of these components. This is accomplished through the use of precomputed databases of response that are generated from a model for the nonlinear system behavior prior to the time that decisions need to be made concerning the disposition of the system. The database is calibrated with a few data points, to account for unmodeled system variables, and then used with an input variable to predict future system behavior. These methods also permit identification of the root causes for observed system behavior. The use of the response databases also permits rapid estimations of uncertainty estimates for the system behavior, such as remaining life estimates, particularly, when subsets of an input variable distribution are passed through the database and scaled appropriately to construct the output distribution. A specific example is the prediction of remaining life for an aircraft component where the model calculates damage evolution, input variables are a crack size and the number of cycles, and the predicted parameters are the actual stress on the component and the remaining life.