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Multimode Optical Fiber Specklegram Pressure Sensor Using Deep Learning
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AI
DOI：10.1109/TIM.2024.3398097.png)
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Abstract
En 中文
Optical fiber pressure sensing is of significant interest for industrial process monitoring and acoustic sensing. However, the direct detection of pressure changes along an optical fiber with minimal crosstalk is a significant challenge due to the low stress-optic coefficient of glass and interferences from temperature, bending, and strain. Here, we demonstrate that deep learning combined with microstructured optical fiber specklegram sensing is an effective approach to overcoming environmental crosstalk for the challenging application of pressure sensing. Specklegrams created from multimode interference are inherently sensitive to both the measurand of interest and other environmental perturbations. By employing a deep neural network, namely a multilayer perceptron, we show that the environmental crosstalk of the specklegram-based pressure sensor can be mitigated. Furthermore, we demonstrate the practical implementation of the machine learning-based pressure sensor where we need to continuously update the model. We show that the network can learn and reject environmental disturbances and predict pressure values correctly through continuous training updates. This technique approaches the instrument error of our calibration pressure gauge, with an error below 3 kPa over a range of 1 MPa.
Keywords:
Sensors
Optical fiber sensors
Optical fiber networks
Optical fiber polarization
Australia
Temperature measurement
Pressure measurement
Deep learning
environmental crosstalk
fiber-optic sensor
machine learning
multimode fiber
pressure sensor
speckle metrology
specklegram sensor
Journal
IF:
5.9
Papers:
1.9W
Citations:
5.8W