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  • JSAP-Optica Joint Symposia 2022 Abstracts
  • (Optica Publishing Group, 2022),
  • paper 20p_C205_5

A machine learning approach for simultaneous measurement of temperature and strain using MSM

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Abstract

In the last two decades, a well-constructed and meaningful research effort has longed where several fiber structures including fiber Bragg grating (FBG), long period grating (LPG), Fabry-Perot (FP) have been engineered for designing different sensing probes with higher degree of performances [1]. Albeit, these probes offer higher resolution but are restricted due to the tricky fabrication process, wider spectral responses, employing costly equipment, lower reproducibility etc. Single-multi-single (SMS) mode and multi-single-multi (MSM) mode structure are popular and fascinating fiber combinations. Such fiber probes can be designed and customized in simple way by employing basic lab equipment. Although extensive scopes are there to study in detail, a small number of pieces of literature are available where MSM is employed as a sensing structure to upsurge the sensitivity. In general, the transfer matrix method is employed to retrieve the data at the time of measuring simultaneously. However, it has been noticed that the coefficients of the matrix change non-linearly with change in temperature and strain which is highly undesirable and may introduce huge errors in measurement. Furthermore, the accuracy in the case of detection is also quite poor. These flaws could be reduced by employing an ANN model [2].

© 2022 Japan Society of Applied Physics, Optica Publishing Group

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