A novel defect depth measurement method based on nonlinear system identification for pulsed thermographic inspection

This paper introduces a new method to improve the reliability and confidence level of defect depth measurement based on pulsed thermographic inspection by addressing the over-fitting problem. Different with existing methods using a fixed model structure for all pixels, the proposed method adaptively detects the optimal model structure for each pixel thus targeting to achieve better model fitting while using less model terms. Results from numerical simulations and real experiments suggest that (a) the new method is able to measure defect depth more accurately without a pre-set model structure (error is usually within 1% when SNR>32 dB) in comparison with existing methods, (b) the number of model terms should be 8 for signals with SNR∈[30dB,40dB] 8–10 for SNR>40 dB and 5–8 for SNR<30 dB, and (c) a data length with at least 100 data points and 2–3 times of the characteristic time usually produces the best results.