Sloshing causes liquid to fluctuate, making accurate level
readings difficult to obtain in dynamic environments. The
measurement system described uses a single-tube capacitive sensor
to obtain an instantaneous level reading of the fluid surface,
thereby accurately determining the fluid quantity in the presence
of slosh. A neural network based classification technique has been
applied to predict the actual quantity of the fluid contained in a
tank under sloshing conditions.
In "A neural network approach to fluid quantity measurement in
dynamic environments," effects of temperature variations and
contamination on the capacitive sensor are discussed, and the
authors propose 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 effectiveness of signal enhancement on
the neural network based signal classification system is also
investigated. Results obtained from the investigation are compared
with traditionally used statistical averaging methods, and proves
that the neural network based measurement system can produce highly
accurate fluid quantity measurements in a dynamic environment.
Although in this case a capacitive sensor was used to demonstrate
measurement system this methodology is valid for all types of
electronic sensors.
The approach demonstrated in "A neural network approach to fluid
quantity measurement in dynamic environments "can be applied to a
wide range of fluid quantity measurement applications in the
automotive, naval and aviation industries to produce accurate fluid
level readings. Students, lecturers, and experts will find the
description of current research about accurate fluid level
measurement in dynamic environments using neural network approach
useful."
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