Composite GW baseline enabled large range temperature compensation method based on singular value decomposition and ADALINE network
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel method using singular value decomposition (SVD) and adaptive filter linear neural (ADALINE) networks to compensate for temperature variations in guided wave (GW) structural health monitoring (SHM) signals. The technique accurately corrects GW signals across a 60°C range, enhancing SHM reliability.
Area Of Science
- Engineering
- Materials Science
- Signal Processing
Background
- Guided wave (GW) based structural health monitoring (SHM) is crucial in aerospace but hindered by environmental factors like temperature variations.
- Temperature fluctuations significantly impact GW signals, reducing SHM accuracy and reliability, limiting practical applications.
- Existing methods struggle to effectively compensate for temperature-induced signal distortions.
Purpose Of The Study
- To propose a novel data-driven method for compensating temperature variation effects on GW signals for SHM.
- To eliminate the dependence on linear correlation in traditional ADALINE methods for signal compensation.
- To enhance the accuracy and reliability of SHM technology under varying environmental conditions.
Main Methods
- A hybrid approach combining singular value decomposition (SVD) and adaptive filter linear neural (ADALINE) network.
- Extraction of singular vector matrices from baseline signals to train the ADALINE network, bypassing linear correlation.
- Generation of a composite baseline set using weight matrices and a selected stored baseline signal for temperature compensation.
Main Results
- Experimental validation on a composite skin structure (-10°C to 50°C) demonstrated accurate temperature compensation.
- The method successfully generated compensation signals over a 60°C temperature interval using a baseline at -10°C.
- Maximum errors in amplitude and phase of compensation signals were below -38 dB and -50 dB, respectively.
Conclusions
- The proposed SVD-ADALINE method effectively compensates for temperature variations in GW signals for SHM.
- This data-driven approach enhances SHM reliability and accuracy across a wide temperature range.
- The method shows significant potential for real-world applications requiring robust structural monitoring under environmental changes.
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