Thermographic Component Evaluation for Remaining Service Life Prediction
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Solution Overview
Problem
Existing non-destructive testing methods for aircraft power plant components do not effectively predict the future performance or service life of components with internal defects such as cracks and voids, despite providing insights into structural integrity.
Innovation Solution
A method using infrared thermography combined with mechanical excitation and machine learning to quantify the remaining service life of components by analyzing thermographic images, where a machine learning algorithm is trained on historical data associating previous images with service lives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing non-destructive testing methods are used to inspect component structural integrity, then defects can be detected, but the future performance and remaining service life of the component cannot be predicted
Solution Approach 1:
The system performs preliminary mechanical excitation and thermal imaging before the component reaches its failure point, capturing thermographic data that correlates with remaining service life. This allows prediction of future performance based on current thermal response characteristics, rather than waiting for actual failure or using post-failure analysis.
Solution Approach 2:
The patent replaces traditional mechanical testing methods that require actual stress loading with a thermal imaging-based assessment system. By using mechanical excitation to induce thermal responses and analyzing these responses through machine learning, the system substitutes direct mechanical performance testing with a non-intrusive thermal field measurement approach.
2Measurement precision
If mechanical excitation is applied to induce thermal response for thermographic imaging, then remaining service life can be estimated, but additional equipment and process complexity are required
Solution Approach 1:
The mechanical excitation device serves multiple functions: it provides structural support for the component, applies controlled excitation forces to induce thermal responses, and acts as a mounting platform for sensors. This multi-functionality reduces the need for separate dedicated testing equipment, thereby reducing overall system complexity despite the advanced measurement capabilities.
Solution Approach 2:
The component itself serves as the test object and the source of information. By using the component's own thermal response to mechanical excitation as the measurement signal, the system eliminates the need for external test specimens, artificial defects, or complex simulation setups. The component's natural thermal behavior under excitation provides the necessary data for service life prediction.
3Measurement precision
If thermographic imaging is used to capture thermal responses, then defect characteristics can be identified, but the location and type of defects affect the accuracy of remaining service life prediction
Solution Approach 1:
The patent introduces thermal response patterns as an intermediary between the physical defect and the remaining service life prediction. Instead of directly measuring defect geometry or material properties, the system captures thermal wave propagation patterns that are influenced by defect characteristics. These thermal patterns serve as mediators that encode defect information in a form that machine learning algorithms can process to predict service life, bypassing the difficulty of direct defect characterization.
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
Enables accurate estimation of the health condition and remaining service life of components, improving reliability in non-destructive testing and health monitoring by predicting future performance based on thermal responses induced by mechanical excitation.
Implementation Method 1
Mechanically exciting the component may include inducing frictional heating at the defect.
Implementation Method 2
acquiring at a computer from an infrared sensor a thermographic image of a part of the component containing the defect and taken while the component exhibits the thermal response
Data Source
AI summary
Systems and methods for non-destructive evaluation of components using infrared thermography are provided. A method includes mechanically exciting the component, and using an infrared sensor to acquire a thermographic image of part of the component containing a defect. Using a machine learning algorithm, a health condition of the component is determined based on the new thermographic image. The machine learning algorithm is trained using machine learning and historical data associating previous thermographic images with previous remaining service lives for the component.