# US 12,602,583 B2 · Method of training a neural network to control an aircraft system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,602,583 B2 - Document ID: US12602583B2 - Status: Granted - Kind: B2 - Application number: 17/686,041 - Filed: 2022-03-03 - Published: 2022-09-08 - Granted: 2026-04-14 - Earliest claimed: 2021-03-04 - Assignees: Airbus Operations GmbH; Airbus SAS - Applicants: Airbus Operations Limited; Airbus (S.A.S.) - Inventors: George Howell; Jean-Marie Dautelle; Andrea Laruelo-Fernandez; William Parr - Web page: https://patentgrep.com/patent/US12602583B2 ## CPC - B60T 8/1703: {for aircrafts} - G06N 3/08: Learning methods - B60T 8/174: characterised by using special control logic, e.g. fuzzy logic {, neural computing} - G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques} - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/082: modifying the architecture, e.g. adding, deleting or silencing nodes or connections - G06N 3/09: Supervised learning - G07C 5/02: Registering or indicating driving, working, idle, or waiting time only (apparatus forming part of taximeters G07B13/00) - G07C 5/0808: {Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005)} - G07C 5/0841: {Registering performance data (recording measured values G01D; information storage G11B)} - B64C 25/44: Actuating mechanisms - B64F 5/60: Testing or inspecting aircraft components or systems - B60T: VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL (electrodynamic brake systems for vehicle, in general B60L; brakes per se, i.e. devices where braking effect occurs, including ultimate brake actuators, F16D); ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE - B64C: AEROPLANES; HELICOPTERS - B64F: GROUND OR AIRCRAFT-CARRIER-DECK INSTALLATIONS SPECIALLY ADAPTED FOR USE IN CONNECTION WITH AIRCRAFT; DESIGNING, MANUFACTURING, ASSEMBLING, CLEANING, MAINTAINING OR REPAIRING AIRCRAFT, NOT OTHERWISE PROVIDED FOR; HANDLING, TRANSPORTING, TESTING OR INSPECTING AIRCRAFT COMPONENTS, NOT OTHERWISE PROVIDED FOR ## Abstract A method of training a neural network to control an aircraft system. The method includes obtaining an operational mode data space representing a set of operational modes for the aircraft system, wherein each operational mode represents a configuration of the aircraft system where performance of an aircraft component is impaired according to an aircraft system model. Each operational mode includes probability data indicating a respective probability of the operational mode occurring, according to the model. The method includes generating a reduced operational mode data space including operational modes having a probability greater than a theoretical operational probability threshold, and generating a training operational mode data space including operational modes within the reduced operational mode data space that have a probability less than a real-world operational probability threshold. The method includes training the neural network using operational modes within the training operational mode data space. ## Claims ### Claim 1 A method of training a neural network to control an aircraft braking system having a plurality of aircraft braking system components, the method comprising: obtaining an operational mode data space representing a set of operational modes for the aircraft braking system, wherein each operational mode represents a configuration of the aircraft braking system where performance of one or more of the plurality of aircraft braking system components is impaired and comprises probability data indicating a respective positive and non-zero probability of the operational mode occurring, according to a model of the aircraft braking system, wherein the plurality of aircraft braking system components comprise one or more of a power supply selector valve and an actuator; generating a reduced operational mode data space limited to the operational modes having a probability greater than a theoretical operational probability threshold and excluding the operational modes having a probability less than the theoretical operational probability; generating a training operational mode data space limited to the operational modes within the reduced operational mode data space that have a probability less than a real world operational probability threshold and excluding the operational modes within the reduced operational mode data space having a probability greater than the real-world operational probability threshold; and training the neural network using operational modes within the training operational mode data space, to control reconfiguration of the braking system by controlling one or more of the power supply selector valve and the actuator. ### Claim 2 (depends on 1) The method according to claim 1, wherein the method comprises stratifying the training operational mode data space to generate a plurality of sub-spaces based on the obtained probability data, selecting a first set of operational modes from the plurality of sub-spaces, selecting a second set of operational modes from the plurality of sub-spaces, the second set of operational modes different from the first set of operational modes, training the neural network using the first set of operational modes, and testing the neural network using the second set of operational modes. ### Claim 3 (depends on 2) The method according to claim 2, wherein each operational mode of the first set of operational modes is different from each operational mode of the second set of operational modes. ### Claim 4 (depends on 2) The method according to claim 2, wherein selecting the first and second sets of operational modes comprises randomly selecting the first and second sets of operational modes from the plurality of sub-spaces. ### Claim 5 (depends on 2) The method according to claim 2, wherein selecting the first and second sets of operational modes comprises selecting operational modes from the plurality of sub-spaces such that each of the first and second sets of operational modes has the same distribution of probabilities as the training operational mode data space. ### Claim 6 (depends on 2) The method according to claim 2, wherein the method further comprises stratifying the plurality of sub-spaces based on a structure of the aircraft braking system to generate a plurality of further sub-spaces, selecting the first set of operational modes from the plurality of further sub-spaces and selecting the second set of operational modes from the plurality of further sub-spaces. ### Claim 7 (depends on 1) The method according to claim 1, wherein the method further comprises generating an operational mode data space using operational modes present in the reduced operational mode data space but absent from the training operational mode data space, and verifying operation of the neural network using operational modes within the operational mode data space. ### Claim 8 (depends on 1) The method according to claim 1, wherein the theoretical operational probability threshold is set such that operational modes present in the operational mode data space but absent from the reduced operational mode data space comprise a cumulative probability less than an acceptable safety level for the aircraft braking system. ### Claim 9 (depends on 1) The method according to claim 1, wherein a size of the operational mode data space is determined based on a maximum acceptable probability of a determined critical operational mode according to the model of the aircraft braking system, and an operational mode having a highest probability according to the model of the aircraft braking system. ### Claim 10 (depends on 1) The method according to claim 1, wherein the neural network comprises a plurality of nodes, and the method comprises monitoring outputs from the plurality of nodes during training of the neural network to determine activity data representing activity of the plurality of nodes, identifying a node or combination of nodes having activity levels below an activity level threshold using the activity data, and modifying the operational modes used to train the neural network based on the identified node or combination of nodes. ### Claim 11 (depends on 10) The method according to claim 10, wherein modifying the operational modes used to train the neural network based on the identified node or combination of nodes comprises utilising a greater number of operational modes from within the training operational mode data space. ### Claim 12 (depends on 10) The method according to claim 10, wherein modifying the operational modes used to train the neural network based on the identified node or combination of nodes comprises adding operational modes to the training operational mode data space from the reduced operational mode data space. ### Claim 13 (depends on 10) The method according to claim 10, wherein modifying the operational modes used to train the neural network based on the identified node or combination of nodes comprises adding operational modes to the training operational mode data space from the operational mode data space. ### Claim 14 (depends on 1) The method according to claim 1, wherein the model of the aircraft braking system comprises a directed graph comprising one or more vertices, and each vertex represents operation of at least one of the aircraft braking system components of the aircraft braking system. ### Claim 15 (depends on 1) The method according to claim 1, wherein the aircraft braking system comprises a braking system, and each operational mode represents a respective set of possible braking component impairments according to a model of the aircraft braking system. ### Claim 16 (depends on 1) The method according to claim 1, wherein operational modes present in the operational mode data space but absent from the reduced operational mode data space define a discarded operational mode data space, and the discarded operational mode data space comprises a cumulative probability less than an acceptable safety level for the aircraft braking system. ### Claim 17 A method of training a neural network to control an aircraft braking system having a plurality of aircraft braking system components, the method comprising: obtaining an aircraft braking system operational mode data space representing a set of operational modes for the aircraft braking system, wherein each operational mode represents a configuration of the aircraft braking system where performance of one or more of the plurality of aircraft braking system components is impaired, according to a model of the aircraft braking system, wherein the plurality of aircraft braking system components comprise one or more of a power supply selector valve and an actuator; obtaining probability data indicative of probabilities of the operational modes occurring, wherein each of the probabilities is positive and greater than a zero probability; comparing obtained probability data for each operational mode to a theoretical operational probability threshold, wherein each of the probabilities is greater than a zero probability; removing operational modes having a probability less than or equal to the theoretical operational probability threshold from the aircraft braking system operational mode data space to leave behind a reduced aircraft braking system operational mode data space, wherein the reduced aircraft braking system operational mode data space is limited to the operational modes from the operational mode data space having a probability greater than the theoretical operational probability threshold and excludes the operational modes in the operational mode data space having a probability equal or less than the theoretical operational probability threshold; comparing obtained probability data for operational modes within the reduced aircraft braking system operational mode data space to a real-world operational probability threshold; removing operational modes having a probability greater than or equal to the real-world operational probability threshold from the reduced aircraft braking system operational mode data space to leave behind a training aircraft braking system operational mode data space, wherein the operational modes remaining in the training aircraft braking system operational mode data space is limited to the operational modes in the reduced aircraft braking system operational mode data space having a probability less than the real-world operational probability threshold; and training the neural network using operational modes within the training aircraft braking system operational mode data space, to control reconfiguration of the braking system by controlling one or more of the power supply selector valve and the actuator. ### Claim 18 (depends on 1) A data carrier comprising machine-readable instructions for the operation of a processor of a computer system to perform the method according to claim 1. ### Claim 19 (depends on 18) A computer system for training a neural network to control the aircraft braking system, the computer system comprising a processor and a data carrier according to claim 18. ### Claim 20 (depends on 1) An aircraft braking system comprising a neural network trained according to the method of claim 1. ### Claim 21 (depends on 1) The method of claim 1, wherein: the generating of the reduced operational mode includes selecting for the reduced operational mode data space the operational modes from the operational mode data space having a probability greater than the theoretical operational probability threshold and excluding for use in the reduced operational mode data space the operational modes in the operational mode data space having a probability equal or less than the theoretical operational probability threshold; and the generating of the training operational mode data space selects for the training operational mode data space the operational modes in the reduced operational mode data space having a probability less than the real-world operational probability threshold and excludes for use in the training operational mode data space operational modes the operational modes in the reduced operational mode data space having a probability equal to or greater than the real-world operational probability threshold.