Optical frequency domain reflectometry-based high-performance distributed sensing empowered by a data and physics-driven neural network

Abstract

Optical frequency domain reflectometry (OFDR) based distributed strain sensors are the preferred choice for achieving accurate strain measurements over extensive sensing ranges while maintaining exceptional spatial resolution. However, the simultaneous realization of high spatial resolution, high strain resolution, large strain range, and an extended sensing range presents an exceedingly challenging endeavor. In this study, we introduce and experimentally demonstrate a data and physics-driven neural network-empowered OFDR system designed to attain high-performance distributed sensing. In our experiments, we successfully maintained an impressive sensing resolution of sub-microstrain (0.91 με) alongside a sharp spatial resolution of sub-millimeter (0.857 mm) across a 140-m sensing range. To the best of our knowledge, this marks the inaugural experimental demonstration of OFDR-based distributed sensing, combining sub-millimeter spatial resolution and sub-με strain resolution across a lengthy sensing range over a hundred meters. This pioneering work unveils new pathways for the development of ultra-high-performance optical fiber sensing systems, paving the way for the next generation of intelligent systems tailored for diverse smart industrial applications.