Synthetic air data output generation

US9932127B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-9932127-B2
Application numberUS-201514962137-A
CountryUS
Kind codeB2
Filing dateDec 8, 2015
Priority dateDec 8, 2015
Publication dateApr 3, 2018
Grant dateApr 3, 2018

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Abstract

Official abstract text for this publication.

In one example, a method includes receiving, over an aircraft data communications bus, a plurality of non-pneumatic inputs corresponding to aircraft operational parameters. The method further includes processing the plurality of non-pneumatic inputs through an artificial intelligence network to generate an air data output value, and outputting the air data output value to a consuming system for use when a pneumatic-based air data output value is determined to be unreliable.

First claim

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The invention claimed is: 1. A method comprising: receiving, over an aircraft data communications bus, a plurality of non-pneumatic inputs corresponding to aircraft operational parameters; processing the plurality of non-pneumatic inputs through an artificial intelligence network to generate an air data output value; and outputting the air data output value to a consuming system for use when a pneumatic-based air data output value is determined to be unreliable; wherein the artificial intelligence network comprises an artificial neural network having at least one internal layer of neurons that apply one or more weights, biases, or transfer functions to each of the plurality of non-pneumatic inputs to generate the air data output value; wherein the artificial neural network is pre-trained to determine the one or more weights, biases, or transfer functions; and wherein processing the plurality of non-pneumatic inputs through the artificial intelligence network to generate the air data output value comprises processing the plurality of non-pneumatic inputs through the artificial neural network without changing the one or more weights, biases, or transfer functions. 2. The method of claim 1 , wherein the plurality of non-pneumatic inputs comprise one or more of an aircraft engine thrust parameter, an aircraft engine throttle setting, a flight control surface position, a flight control surface loading, an aircraft fuel usage rate, an aircraft weight, a landing gear position, an aircraft mass balance, an aircraft acceleration, and an aircraft angular rate. 3. The method of claim 1 , wherein the generated air data output value is selected from a group comprising an aircraft calibrated airspeed, an aircraft true airspeed, an aircraft Mach number, an aircraft pressure altitude, an aircraft angle of attack, an aircraft vertical speed, and an aircraft angle of sideslip. 4. The method of claim 1 , wherein the artificial neural network is a feed-forward neural network. 5. The method of claim 1 , further comprising: receiving the pneumatic-based air data output value from a pneumatic-based air data system; and identifying whether the received pneumatic-based air data output value is determined to be reliable or whether the received pneumatic-based air data output value is determined to be unreliable; wherein processing the plurality of non-pneumatic inputs through the artificial intelligence network to generate the air data output value further comprises: processing the non-pneumatic inputs and the received pneumatic-based air data output value through the artificial intelligence network to generate the air data output value when the received pneumatic-based air data output value is determined to be reliable; and processing the non-pneumatic inputs without the received pneumatic-based air data output value through the artificial intelligence network to generate the air data output value when the received pneumatic-based air data output value is determined to be unreliable. 6. The method of claim 1 , further comprising: outputting the air data output value to a consuming system that determines whether the pneumatic-based air data output value is unreliable based at least in part on the generated air data value. 7. The method of claim 1 , further comprising: determining whether the pneumatic-based air data output value is unreliable. 8. A synthetic air data system comprising: at least one processor; and non-transitory computer-readable memory encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to: receive, over an aircraft data communications bus, a plurality of non-pneumatic inputs corresponding to aircraft operational parameters; process the plurality of non-pneumatic inputs through an artificial intelligence network to generate an air data output value, wherein the artificial intelligence network is a pre-trained artificial neural network having at least one internal layer of neurons that apply one or more weights, biases, or transfer functions to each of the plurality of non-pneumatic inputs to generate the air data output value without changing the one or more weights, biases, or transfer functions; and output the air data output value to a consuming system for use when a pneumatic-based air data output value is determined to be unreliable. 9. The synthetic air data system of claim 8 , wherein the plurality of non-pneumatic inputs comprise one or more of an aircraft engine thrust parameter, an aircraft engine throttle setting, a flight control surface position, a flight control surface loading, an aircraft fuel usage rate, an aircraft weight, a landing gear position, an aircraft mass balance, an aircraft acceleration, and an aircraft angular rate. 10. The synthetic air data system of claim 8 , wherein the computer-readable memory is encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to process the plurality of non-pneumatic inputs through the artificial intelligence network to generate the air data output value that is selected from a group comprising an aircraft calibrated airspeed, an aircraft true airspeed, an aircraft Mach number, an aircraft pressure altitude, an aircraft angle of attack, an aircraft vertical speed, and an aircraft angle of sideslip. 11. The synthetic air data system of claim 8 , wherein the computer-readable memory is further encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to: receive the pneumatic-based air data output value from a pneumatic-based air data system; and identify whether the received pneumatic-based air data output value is determined to be reliable or whether the received pneumatic-based air data output value is determined to be unreliable; and wherein the computer-readable memory is encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to process the plurality of non-pneumatic inputs through the artificial intelligence network to generate the air data output value by at least causing the synthetic air data system to: process the non-pneumatic inputs and the received pneumatic-based air data output value through the artificial intelligence network to generate the air data output value when the received pneumatic-based air data output value is determined to be reliable; and process the non-pneumatic inputs without the received pneumatic-based air data output value through the artificial intelligence network to generate the air data output value when the received pneumatic-based air data output value is determined to be unreliable. 12. The synthetic air data system of claim 8 , wherein the computer-readable memory is further encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to output the air data output value to a consuming system that determines whether the pneumatic-based air data output value is unreliable based at least in part on the generated air data value. 13. The synthetic air data system of claim 8 , wherein the computer-readable memory is further encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to determine whether the pneumatic-based air data output value is unreliable. 14. A method comprising: receiving, over an aircraft data communications bus, a plurality of non-pneumatic inputs corresponding to aircraft operational parameters; processing the plurality of non-pneumatic inputs throu

Assignees

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Classifications

  • Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft (application of speed-measuring devices for measuring volume of fluid G01F) · CPC title

  • for measuring speed of fluids; for measuring speed of bodies relative to fluids (for measuring volume flow G01F25/10) · CPC title

  • G01P5/16Primary

    using Pitot tubes {, e.g. Machmeter} · CPC title

  • Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005) · CPC title

  • B64D43/00Primary

    Arrangements or adaptations of instruments · CPC title

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What does patent US9932127B2 cover?
In one example, a method includes receiving, over an aircraft data communications bus, a plurality of non-pneumatic inputs corresponding to aircraft operational parameters. The method further includes processing the plurality of non-pneumatic inputs through an artificial intelligence network to generate an air data output value, and outputting the air data output value to a consuming system for…
Who is the assignee on this patent?
Rosemount Aerospace Inc
What technology area does this patent fall under?
Primary CPC classification G01P5/16. Mapped technology areas include Physics.
When was this patent published?
Publication date Tue Apr 03 2018 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 2 related publications on this page (citations in our corpus or others sharing the same primary CPC).