Deep learning method for detection of structural microcracks by brillouin scattering based distributed optical fiber sensors

Abstract

Brillouin scattering (BS)–based distributed optical fiber sensors (DOFS) provide distributed sensing capabilities by monitoring the strain along entire segments of structures. Large cracks, such as those with large crack opening displacements (COD) can be detected by strain peaks or singularities along the measurement length of distributed sensors. Microcracks do not provide visible pronounced local peaks along the length of measured distributed strains. The peaks corresponding to microcracks are submerged within the measurement noise due to low signal-to-noise ratio (SNR) of BS systems. Deep learning (DL) methods have the potential to automatically extract feature representations from data exhibiting lower SNRs, and improve classification accuracy. Accordingly, a novel DL method is proposed in this study to improve the crack detection sensitivity of the BS-based DOFS. Development of the proposed DL method includes construction of model architecture, design of a training algorithm and the detection process. A 15 m-long wide-flange steel beam with artificial defects is built and employed in this study. A comprehensive experimental program is undertaken in order to train, validate and test the generality of the proposed DL method. Experimental results demonstrate that the DL method is capable of extracting highly discernable microcrack features from the distributed strains, and distinguish the crack-induced local peaks from the noise. Microcracks with CODs as small as 23 microns are accurately detected in the present work.