A neural network approach to fluid quantity measurement in dynamic environments
Sloshing causes liquid to fluctuate, making accurate level readings difficult to obtain. Ill this paper, a measurement system has been described that can accurately determine fluid quantity in the presence of slosh. The measurement system uses a single-tube capacitive sensor to obtain instantaneous level of the fluid surface. A neural network based classification technique has been applied to predict the actual quantity of the fluid under sloshing conditions. Effects of temperature variations and contamination on the capacitive sensor have been discussed and it is proposed that these effects can also be eliminated with the proposed neural network based classification system. To examine the performance of the classification system, many field trials were carried out on a running vehicle at various tank volume levels that range from 5 L to 50 L The paper also investigates the effectiveness of signal enhancement on the neural network based signal classification system. Signal enhancement is performed using selected signal smoothing functions such Moving Mean, Moving Median, and Wavelet filters. Results obtained from the investigation are compared with traditionally used statistical averaging methods, and it proved that the neural network based measurement system can produce highly accurate fluid quantity measurements in a dynamic environment. The approach demonstrated herein will enable a wide range of fluid quantity measurement applications in the fields of automotive, naval and aviation industries to produce accurate fluid level readings. (C) 2010 Elsevier Ltd. All rights reserved.
signal smoothing accurate fluid quantity measurement smart capacitive sensor liquid slosh backpropagation neural network