Photonic thermometers – which measure temperature using light – have been around in optical fiber form for decades. These devices, called fiber Bragg gratings, are embedded in commercially available fibers thinner than a human hair, similar to the ones ubiquitous in network communications.
Inexpensive and with the ability to be embedded into structures that might otherwise be difficult to access, the sensors are used routinely in civil infrastructure (which includes bridges and tunnels) and in the oil and gas industries. But they aren’t quite accurate enough for some other applications that might otherwise make use of them, including monitoring of freezers, ovens, medical-grade refrigerators, and certain industrial processes.
One significant hit to the sensors’ accuracy comes from long-term drift. This occurs when, over time, the same temperature results in a different reading. Recalibrating the sensor every few months fixes the problem, but this can be expensive and time-consuming, especially if the sensor is buried in concrete or otherwise embedded permanently in a structure.
A fiber Bragg grating is a sensor etched into a fiber optic cable. This animation shows the basic operating principle. At one temperature (say, 20 degrees Celsius), the grating allows all wavelengths except a narrow band (in this example, green light) to pass through the fiber. At another temperature (say, 25 degrees Celsius), the grating allows all but a different band of wavelengths (in this example, red light) to pass through.
Credit: Kristen Dill/NIST
In a new paper published this week in Sensors and Actuators A: Physical, a scientist from the National Institute of Standards and Technology (NIST) describes how he has used machine learning techniques to predict the long-term drift of existing fiber Bragg-grating sensor technology. The proof-of-concept work shows how a type of hooked photonics rescue
