Experimental Dataset for Fiber Optic Specklegram Sensing Under Thermal Conditions and Use in a Deep Learning Interrogation Scheme

Francisco J. Vélez (), Juan D. Arango, Víctor H. Aristizábal, Carlos Trujillo and Jorge A. Herrera-Ramírez
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Francisco J. Vélez: Facultad de Ingeniería, Universidad Cooperativa de Colombia, Medellín 050012, Colombia
Juan D. Arango: Facultad de Ciencias Exactas y Aplicadas, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia
Víctor H. Aristizábal: Facultad de Ingeniería, Universidad Cooperativa de Colombia, Medellín 050012, Colombia
Carlos Trujillo: School of Applied Sciences and Engineering, EAFIT University, Medellín 050022, Colombia
Jorge A. Herrera-Ramírez: Facultad de Ciencias Exactas y Aplicadas, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia

Data, 2025, vol. 10, issue 4, 1-8

Abstract: This dataset comprises specklegram images acquired from a multimode optical fiber subjected to varying thermal conditions. Designed for training neural networks focused on developing Fiber Optic Specklegram Sensors (FSSs), these experimental data enable the detection of changes in speckle patterns corresponding to applied temperature variations. The dataset includes 24,528 images captured over a temperature range from 25 °C to 200 °C, with incremental steps of approximately 0.175 °C. Key acquisition parameters include a wavelength of 633 nm, a sensing zone length of 20 mm, and a multimode fiber with a core diameter of 62.5 μm. This dataset supports developing and validating temperature-sensing models using fiber optic technology and can facilitate benchmarking against other experimental or synthetic datasets. Finally, an implementation is presented for utilizing the dataset in a deep learning interrogation scheme.

Keywords: optical sensors; specklegram; fiber optic sensing; deep learning; temperature measurement (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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