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Comparative performance evaluation of classical methods and a deep learning approach for temperature prediction in fiber optic specklegram sensors
F.J. Vélez 1,2, J.D. Arango 3, V.H. Aristizábal 1, C.A. Trujillo 2, J. Herrera-Ramírez 3

1 Facultad de Ingeniería, Universidad Cooperativa de Colombia,
Calle 50 N° 41 – 34, 050010, Medellín, Colombia;
2 School of Applied Sciences and Engineering, EAFIT University,
Carrera 49 N° 7 Sur –50, 050022, Medellín, Colombia;
3 Facultad de Exact and Applied Sciences, Instituto Tecnológico Metropolitano,
Calle 73 N° 26A – 354, 050054, Medellín, Colombia

 PDF, 2277 kB

DOI: 10.18287/2412-6179-CO-1467

Pages: 689-695.

Full text of article: English language.

Abstract:
In this study, an algorithm based on convolutional neural networks is employed as an interrogation method for a fiber specklegram sensor. This algorithm is compared with conventional interrogation methods, including correlation between images, measurement of optical power, and radial moments. Fiber specklegram sensors have room for improvement as conventional methods only consider a single characteristic of the specklegram for variable prediction, thus failing to leverage the full spectrum of information within the specklegram. Consequently, the approach put forth here introduces a convolutional neural network for the extraction of specklegram features, accompanied by an artificial neural network for variable regression. The specklegrams used in this investigation are obtained through simulating temperature disturbances in a multimode fiber using the Finite Elements Method. The results reveal prediction RMSE errors ranging from 10.26°C for the first radial moment to 1.42°C for the proposed algorithm. These findings underscore the effectiveness of the proposed strategy in enhancing sensor performance and robustness, all while upholding their cost-efficiency.

Keywords:
fiber specklegram sensors; finite element method; interrogation methods; deep learning; convolutional neural network.

Citation:
Vélez FJ, Arango JD, Aristizábal VH, Trujillo CA, Herrera-Ramírez J. Comparative performance evaluation of classical methods and a deep learning approach for temperature prediction in fiber optic specklegram sensors. Computer Optics 2024; 48(5): 689-695. DOI: 10.18287/2412-6179-CO-1467.

Acknowledgements:
This work was partially funded by Universidad Cooperativa de Colombia (UCC) (INV3612); Instituto Tecnológico Metropolitano (ITM) (P20222), EAFIT University and MINCIENCIAS National Doctorates program.

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