The Application of Kernel Ridge Regression for the Improvement of a Sensing Interferometric System

TL;DR

This paper shows how machine learning can improve the measurement range of interferometric sensors used for temperature sensing.

Contribution

Applying Kernel Ridge Regression with a Gaussian kernel to significantly widen the measurement range of interferometric sensors.

Findings

KRR with a Gaussian kernel achieved a root-mean-square error of 0.094 °C for temperature estimation.

The measurement range was widened by a factor of eight compared to traditional methods.

Four kernel functions were tested to estimate the response variable using spectral features.

Abstract

Sensors based on interferometric systems have been studied due to their wide range of advantages, such as high sensitivity. For these types of sensors, traditional methods, which generally depend on the linear sensitivity of one variable, have been used to determine the measurand parameter. Usually, these methods are only effective for short measurement ranges, which is one of the main limiting factors of these sensors. In this work, it is shown that Kernel Ridge Regression (KRR), which is a machine learning method, can be applied to improve the range of measurement of multilayer interferometric sensors. This method estimates the value of a response variable (temperature) based on a set of spectral features, which are transformed by means of kernel functions. Here, these features were the wavelength positions and maximum amplitudes of some peaks of the interference spectrum of the sensing system. To sustain the application of the method, four kernel functions were used to estimate the values of the response variable. Finally, the results show that by implementing KRR with a Gaussian kernel, the temperature could be estimated with a root-mean-square error of 0.094 °C for the measurement range from 4.5 to 50 °C, which indicates that it was widened by a factor of eight compared with traditional methods.

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