Fluid level measurement in dynamic environments using a single ultrasonic sensor and Support Vector Machine (SVM)

Abstract

A fluid level measurement system to accurately determine fluid levels in dynamic environments has been described. The measurement system is based on a single ultrasonic sensor and Support Vector Machine (SVM) based signal processing and classification scheme. For exemplification of the measurement system in dynamic environments, the novel measurement system is experimented and verified on a fuel tank of a running vehicle. The effects of slosh and temperature variations on the acoustic sensor based measurement system are reduced using the novel approach. The novel approach is based on ν-SVM classification method with the Radial Basis Function (RBF) to compensate for the measurement error induced by the sloshing effects in the tank due to the motion of the moving vehicle. In this approach, raw sensor signals are differentiated after smoothing with some selected pre-processing filters, namely, Moving Mean, Moving Median, and Wavelet filter. The derivative signal is then transformed into Frequency Domain to reduce the size of input features before performing the signal classification with SVM. Field trials were performed on actual vehicle under normal driving conditions at various fuel volumes ranging from 5 L to 50 L to acquire sample data from the ultrasonic sensor for the training of SVM model. Further drive trials were conducted to obtain data to verify the SVM results. A comparison of the accuracy of the predicted fluid level obtained using SVM and the pre-processing filters is provided. It is demonstrated that the ν-SVM model using the RBF kernel function and the Moving Median filter has produced the most accurate outcome compared with the other signal filtration methods in terms of fluid level measurement.