Component Distress Prediction Using Iterative Damage Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting distress on physical components, particularly in high-stress environments like aircraft engines, lack accuracy and reliability, leading to unplanned maintenance and operational disruptions.
Innovation Solution
A method and system for predicting distress on physical components by obtaining distress data, determining a distress rank, incorporating environmental and operational data, formulating a kernel using a cumulative damage model, and iteratively tuning a predictive model until the difference between the distress output and rank is within a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing prediction methods are used, then the process is simple, but the prediction accuracy and reliability are insufficient
Solution Approach 1:
The prediction system is segmented into distinct functional modules: distress data acquisition module, environmental data acquisition module, operational data acquisition module, kernel formulation module using cumulative damage model, and iterative tuning module. This segmentation allows complex prediction tasks to be divided into manageable components, improving accuracy while maintaining systematic organization.
Solution Approach 2:
The predictive model employs dynamic parameter adjustment through iterative tuning, where kernel parameters are continuously optimized based on the difference between predicted distress output and actual distress rank. This dynamic adaptation enables the system to improve prediction accuracy by adjusting parameters in response to performance feedback.
2Reliability
If a simple model is used, then the model is easy to implement, but it cannot provide accurate distress predictions
Solution Approach 1:
The system implements a feedback mechanism where the predicted distress output is compared against actual distress rank, and the difference (error) is used to adjust kernel parameters. This closed-loop feedback process continues iteratively until the prediction error falls within an acceptable threshold, thereby improving prediction reliability through continuous optimization.
Solution Approach 2:
The model performs preliminary actions by formulating the kernel using cumulative damage models before iterative tuning begins. This preliminary structuring based on established damage theories provides a solid foundation that guides subsequent parameter optimization, making the complex tuning process more systematic and reliable.
3Measurement precision
If multiple data types are incorporated, then the prediction comprehensiveness improves, but the data processing complexity increases
Solution Approach 1:
The system merges multiple data sources including distress data, environmental data, and operational data into a unified predictive framework. By combining these diverse data types through the kernel formulation, the model achieves comprehensive distress assessment that leverages information from all sources simultaneously, improving measurement accuracy through data integration.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An apparatus (10) and method (100) for predicting distress on a physical component (30). The method (100) can include obtaining (102) distress data. The distress data can be used to determine (104) a distress rank. The distress rank can be compared (112) to a distress output provided by a kernel (108) that use parameters related to the physical component (30). The comparison (112) can result in a predictive model (120) for the physical component (30).