Long Short-Term Memory Network Enhances Performance of Optical Fiber Strain Sensors Based on Optical Vernier Effect

High sensitivity and large experiment measurement range are a pair of mutually restricted physical characteristics that restrict the development and application of optical fiber interferometric sensors. In this paper, we achieve high sensitivity and a large measurement range with optical fiber strain sensors through Long Short-Term Memory (LSTM) networks, thereby effectively overcoming the problem of free spectral range (FSR) limitation or interference spectrum overlap existing in traditional peak tracking methods. LSTM networks can accurately identify and map the complex relationships between full spectral features and strain, enabling a measurement range that far exceeds approximately 5 times that of a Sagnac interferometer based on the Vernier effect using the traditional peak tracking method. This approach achieves a significantly expanded strain measurement range and improves the stability and reliability of optical fiber sensing. This new method not only maintains high sensitivity and a large measurement range but also avoids complicated and cumbersome data processing in traditional peak tracking methods. More importantly, the whole waveband of the spectrometer can be used, which can theoretically achieve an ultra-large measurement range and even identify interference dips that are difficult to distinguish with the naked eye. This method can further promote the application and development of optical fiber interference sensors.