RESUMEN
Guided wave structural health monitoring is widely researched for remotely inspecting large structural areas. To detect, locate, and characterize damage, guided wave methods often compare data to a baseline signal. Yet, environmental variations create large differences between the baseline and the collected measurements. These variations hide damage signatures and cause false detection. Temperature compensation algorithms, such as baseline signal stretch and the scale transform have been used to optimally realign data to a baseline. While these methods are effective in some conditions, their performance deteriorates in the presence of large temperature variations, long propagation distances, and high frequencies. In this paper, we use dynamic time warping to better align guided wave data and to overcome these errors. When compared with stretch-based methods, we show that the dynamic time warping is more robust to the above-mentioned errors and more accurately detects damage with weak ultrasonic signatures.
