Performance assessment of discrete wavelet transform for de-noising of FBG sensors signals embedded in asphalt pavement

In recent years, the Fiber Bragg Grating (FBG) sensor technology has been increasingly utilized as an optical measurement system in various engineering applications, particularly for structural health monitoring (SHM) purposes. This trend can be attributed to the inherent benefits of FBG sensors, such as their small size, immunity to electromagnetic interference, resistance to corrosion, and high accuracy and sensitivity. Various factors cause noise in the FBG sensor signal, which has a significant effect on measurement precision. As a result, de-noising plays an important role in the use of FBG sensor systems. In this study, strain data collected from FBG sensors embedded in a road section were used to evaluate the performance of discretized wavelet transform (DWT) for denoising FBG signals. The presence of noise poses a significant challenge in accurately measuring low-amplitude strains and light loads. To address this issue, various approaches have been investigated, including the selection of appropriate mother wavelets, levels of decomposition, thresholding functions, and thresholding selection approaches, with the aim of identifying the optimal parameters for effective denoising. The results show that FBG signals could be denoised successfully and low amplitude strains appeared completely without any loss of valuable data.

Automated summary

In recent years, the use of Fiber Bragg Grating (FBG) sensor technology has become increasingly popular in various engineering applications, especially for structural health monitoring purposes. FBG sensors have advantages such as small size, immunity to electromagnetic interference, resistance to corrosion, and high accuracy and sensitivity. However, the presence of noise in FBG sensor signals affects measurement precision, making denoising an important aspect of FBG sensor systems. This study evaluated the performance of discretized wavelet transform (DWT) for denoising FBG signals using strain data collected from FBG sensors embedded in a road section. The results showed successful denoising of FBG signals and preservation of low amplitude strains without any loss of valuable data.