Experimental Demonstration of Peak Wavelength Measurement of Multiplexing Fiber Bragg Gratings Using Convolutional Neural Network

We propose a peak detection method for measuring fiber Bragg gratings (FBGs) using convolutional neural network (CNN) to improve the performances of wavelength division multiplexing. In wavelength division multiplexing, each FBG occupies a certain wavelength range; therefore, the number of FBGs that can be installed is limited by the wavelength band of the light source. To address this issue, methods for overlapping multiple FBGs of the same wavelength within a single occupied wavelength range have been studied. This contributes to improving the limit of multipoint FBG’s manifold. However, this method results in the complex overlapping of multiple FBG reflectance spectra, making it difficult to accurately measure the peak wavelengths of individual FBGs using conventional peak detection methods. Therefore, we developed a peak detection process using CNN, which is suitable for identifying unique feature data. Each FBG of the same wavelength was characterized to have a unique spectral shape by assigning a different full-width at half-maximum (FWHM) values to each. We introduced noise-additive learning, a well-known method of data augmentation that increases tolerance to variations in the experimental signal. As a result, the standard deviation for peak wavelength detection significantly improved to 2.8 pm and the strain measurements with three complex overlapping FBGs were successfully demonstrated. The CNN model is the first to solve the problem of three overlapping FBGs for arbitrary wavelength changes. Furthermore, the developed peak detection process was found to be applicable to measurements that combined multiplexing of FBGs of either identical or different wavelengths.