Peak detection of spectrally-overlapped fibre Bragg gratings using an autoencoder convolutional neural network

Gabriel Rudloff; Marcelo A. Soto

doi:10.1117/12.2679924

23 May 2023 Peak detection of spectrally-overlapped fibre Bragg gratings using an autoencoder convolutional neural network

Gabriel Rudloff, Marcelo A. Soto

Author Affiliations +

Proceedings Volume 12643, European Workshop on Optical Fibre Sensors (EWOFS 2023); 126433B (2023) https://doi.org/10.1117/12.2679924
Event: European Workshop on Optical Fibre Sensors (EWOFS 2023), 2023, Mons, Belgium

Abstract

This paper presents a machine learning (ML) solution to detect the peak wavelength of fibre Bragg grating (FBG) sensors multiplexed with overlapped reflection spectra, and using a serial topology. ML solutions generally require high-quality, high-volume datasets, which can be difficult to obtain in some scenarios. In contrast, here the proposed model is a sparse autoencoder convolutional neural network that can be trained using only the joint reflection spectrum containing all multiplexed FBGs, without information on the spectral position of each sensor. The technique is first verified through simulations and then with experimental data using two wavelengthmultiplexed FBG sensors in series. Comparing with existing methods, results verify that the proposed model has promising adaptation capability under multiple simulation scenarios, outperforming existing methods, whilst the model matches one of the best existing approaches when using experimental data.

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Gabriel Rudloff and Marcelo A. Soto "Peak detection of spectrally-overlapped fibre Bragg gratings using an autoencoder convolutional neural network", Proc. SPIE 12643, European Workshop on Optical Fibre Sensors (EWOFS 2023), 126433B (23 May 2023); https://doi.org/10.1117/12.2679924

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KEYWORDS

Fiber Bragg gratings

Data modeling

Sensors

Simulations

Bragg wavelengths

Convolutional neural networks

Error analysis

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