Machine learning techniques for liquid level estimation using FBG temperature sensor array

Abstract This paper proposes the use of the fiber Bragg grating (FBG) temperature sensors array to estimate the fluid level. The tank is 100 cm in height and 30 cm in width, with 9 FBG sensors distributed along with the tank height. This work proposes level estimation in two steps: level detection (classification) and level estimation (regression). The level detection consists of finding under which FBG the level is. We dichotomize the classes in water and not water: air. For the detection, we use the following Machine Learning (ML) algorithms: Logistic Regression (LogR), Decision Tree (DT), and Support Vector Machine (SVM). We chose the algorithms based on their usability in literature and theoretical consolidation. The algorithm with the best results among the ones tested is DT, resulting in an average accuracy of 89.54%. For the level estimation, we use the wavelength shift in conjunction with the classification obtained via DT. The level estimation consists of estimating in cm the location of the level. The algorithms chosen are Weighted Linear Regression (WLR), Support Vector Regression (SVR), SVR with kernel selection minimize cost (SVRmin). Each of the applied algorithms presented better results in different level intervals. Thus, we propose the Mixed Model (MM), which selects the lowest Root Mean Square Error (RMSE) among the tested regression algorithms at each level and associates it with it. The MM has a RMSE of 3.56 cm, which is approximately four times smaller than when using WLR. The SVM and SVMmin have RMSE of 6.28 cm and 6.14 cm, respectively.