Simmonds Precision Products, Inc.

Bfgoodrich Aerospace Fuel and Utility Systems

Radoslaw R Zakrewski

Mark George Walker

Bradley F Eid

Luis Carlos Rabelo

Liquid gauging apparatus using a time delay neural network for determining a quantity of liquid in a container that is not directly measurable by sensors is disclosed. The apparatus comprises a plurality of sensors and a processor. Each of the sensors are capable of measuring a respective parameter of the liquid and for producing a time varying sensor output signal representative of the respective parameter measured thereby. The processor is programmed to process the sensor output signals by a time delay neural network algorithm to determine a current quantity of the liquid in the container based on current and past parameter measurements of the sensor output signals. Also disclosed is a method of training a time delay neural network algorithm for computing a quantity of liquid in a container from current and past liquid parameter sensor measurements. The method comprises the steps of: establishing a dynamic model of liquid behavior in the container and parameter measurements of the liquid behavior sensed by a plurality of sensors; deriving from the dynamic model training data sets for a plurality of liquid quantity values, each data set comprising current and past liquid parameter sensor measurement values corresponding to a liquid quantity value of the plurality, and the corresponding liquid quantity value; and training the time delay neural network algorithm with the derived training data sets.

Liquid gauging apparatus using a time delay neural network

A fault tolerant liquid measurement system includes a plurality of sensors for measuring parameters of a liquid in a container; each sensor generating a measurement signal representative of the liquid parameter measured thereby. The sensors are grouped into a number of sets, each set including some sensors of another set. The measurement signals of each set of sensors are processed in a processor to determine for each set of sensors a first estimate signal representative of a likelihood of measurement signal validity for the measurement signals of the corresponding set, and a second estimate signal representative of liquid quantity in the container based on the measurement signals of the corresponding set. The processor further determines a third estimate signal of liquid quantity in the container based on a function of the first and second estimate signals.

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South Burlington, VT, United States of America

Radoslaw R Zakrewski