Light intensity optimization of optical fiber stress sensor based on SSA-LSTM model

Abstract

In order to further improve the measurement range and accuracy of optical fiber stress sensor based on the interference between rising vortex beam and plane wave beam, a new stress demodulation model is designed. This model proposes a method to optimize the long-term and short-term memory network (LSTM) model by using sparrow search algorithm (SSA), extract the main characteristics of the influence of various variables on optical fiber stress sensor, and fit the relationship between sensor stress and beam phase difference. This method is an attempt of the deep learning model LSTM in the study of stress mediation model. There are very few related studies, and it is very necessary to fill this gap. In the experiment, the SSA-LSTM neural network is trained by using the data of stress and phase difference measured by the optical fiber stress sensor. The test results show that the mean error of SSA-LSTM neural network is less than that of LSTM neural network, which shows that the combination of SSA-LSTM model and optical fiber stress sensor can make its measurement accuracy higher, The algorithm can more effectively reduce the influence of the surrounding environment and the influence of the light source fluctuation on the measurement range and accuracy of the optical fiber sensor, and has good practical application value. It is proved that the deep learning LSTM neural network has good application value in the light intensity optimization of optical fiber stress sensor.

Temperature Demodulation for an Interferometric Fiber-Optic Sensor Based on Artificial Bee Colony–Long Short-Term Memory

“…As one of the classical deep learning models, long short-term memory (LSTM) has a strong feature extraction capability and powerful nonlinear mapping capability. It is capable of efficiently extracting critical measurement features from one-dimensional signals, making LSTM highly promising for signal demodulation [10]. Moreover, by using the feedback mechanism during the LSTM network training process, the nonlinear correlation between phase signals and temperature can be continuously corrected until the error of the demodulation result is minimized.…”