Aircraft Power Plant Component Life Prediction From Induced Vibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing non-destructive testing methods for aircraft power plant components do not adequately predict future performance based on structural integrity, failing to account for internal defects that affect service life.
Innovation Solution
A method using induced vibration and machine learning to estimate the remaining service life of components by acquiring a measured response, employing a computer-implemented trained model to determine the service life based on historical data associating previous responses with previous remaining service lives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing non-destructive testing methods are used to assess structural integrity, then component inspection is possible, but future performance prediction is not achieved
Solution Approach 1:
The system uses historical data from previous component inspections and service life outcomes to train machine learning models. These models provide feedback predictions about future service life based on current structural integrity assessments, creating a closed-loop system that continuously improves prediction accuracy.
Solution Approach 2:
The patent replaces traditional mechanical inspection methods with a computational approach using machine learning algorithms. The system substitutes physical testing with digital modeling and data analysis to predict service life, transitioning from purely mechanical assessment to an integrated computational science approach.
2Measurement precision
If internal defects are detected using traditional NDT methods, then structural integrity is assessed, but service life estimation remains inaccurate
Solution Approach 1:
The system transforms inspection data from traditional binary defect detection into continuous service life prediction parameters. By converting defect presence/absence into quantitative service life estimates using machine learning, the system creates a gradient of risk assessment that enables precise service life determination.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between traditional NDT inspection data and service life prediction. This intermediary layer processes structural integrity assessments and translates them into accurate service life estimates, bridging the gap between inspection and prediction.
3Reliability
If components are inspected frequently to ensure safety, then reliability is maintained, but operational time is lost
Solution Approach 1:
The system performs preliminary service life prediction using machine learning models before actual component failure or inspection is needed. By predicting remaining service life in advance, the system enables planned maintenance scheduling that prevents failures while minimizing unnecessary inspection time.
Solution Approach 2:
The inspection and maintenance strategy transitions from static fixed-interval inspections to dynamic condition-based scheduling. The system continuously updates service life predictions based on component health data, adjusting inspection timing dynamically to optimize both safety and time efficiency.
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
Enhances the reliability of non-destructive testing by predicting future performance and identifying internal defects, improving the accuracy of service life estimation for aircraft power plant components.
Implementation Method 1
inducing an induced vibration in the component
Implementation Method 2
acquiring a measured response to the induced vibration in the component
Data Source
AI summary
Methods and systems for estimating a remaining service life of a component of an aircraft power plant are provided. A method includes inducing a vibration in the component, and measuring a response to the induced vibration in the component. A computer-implemented trained model is used to determine an estimated remaining service life for the component based on the measured response. An output indicative of the estimated remaining service life of the component is generated.