# US 12,006,062 B2 · Method and system for reducing air-to-ground data traffic communicated from an aircraft

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- Number: US 12,006,062 B2
- Document ID: US12006062B2
- Status: Granted
- Kind: B2
- Application number: 16/874,836
- Filed: 2020-05-15
- Published: 2021-11-18
- Granted: 2024-06-11
- Earliest claimed: 2020-05-15
- Assignees: The Boeing Company
- Applicants: The Boeing Company
- Inventors: Harrison Kuo; Liessman E. Sturlaugson
- Web page: https://patentgrep.com/patent/US12006062B2

## CPC

- B64D 45/00: Aircraft indicators or protectors not otherwise provided for
- B64F 5/60: Testing or inspecting aircraft components or systems
- G06N 20/00: Machine learning
- B64D 2045/0085: {Devices for aircraft health monitoring, e.g. monitoring flutter or vibration}
- G07C 5/008: {communicating information to a remotely located station (transmission systems for measured values G08C)}
- B64D: EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
- 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
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- 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

## Abstract

A method includes receiving, by an equipment monitoring system (EMS), first training data and second training data from a plurality of sensors of an article over a first operating interval and over a second operating interval of the article, respectively, appending buffer data to the first training data, and the second training data to the buffer data to provide extended training data, and clustering the extended training data into a plurality of data clusters associated with operational states of the article. Subsequent to clustering, the EMS receives operational data associated with the plurality of sensors of the article over a third operating interval. The EMS determines a particular data cluster of the plurality of data clusters to which the operational data belongs, and communicates data indicative of an operational state of the article associated with the particular data cluster to a control system.

## Claims

### Claim 1

A method implemented by a computing system, the method comprising:
receiving, by an equipment monitoring system (EMS) of an aircraft, first training data from a plurality of sensors of the aircraft over a first operating interval of the aircraft during a first flight, and second training data from the plurality of sensors of the aircraft over a second operating interval of the aircraft during a second flight;
appending, by the EMS, first buffer data between the first training data and the second training data to provide extended training data;
determining, by the EMS, whether the extended training data reaches a threshold extent of data;
appending, by the EMS, second buffer data to the extended training data when the extended training data is less than the threshold extent of data and repeat training data collection from the plurality of sensors of the aircraft over a third operating interval;
based on determining that the extended training data reaches the threshold extent of data, clustering, by the EMS executing a multivariate window-based clustering algorithm to train the multivariate window-based clustering algorithm for the aircraft with training data that spans multiple flights of the aircraft, the extended training data into a plurality of data clusters associated with a plurality of operational states of the aircraft, such that values of sensor data from the plurality of sensors in the first training data and the second training data received during each operating interval are clustered according to being associated with a same operational state of the aircraft, wherein the plurality of operational states include valid operational states and invalid operational states and specifying values of the first buffer data so that the first buffer data clusters into an invalid operational state;
subsequent to clustering, receiving, by the EMS, operational data output from the plurality of sensors of the aircraft over a fourth operating interval;
determining, by the EMS executing the trained multivariate window-based clustering algorithm, a particular data cluster of the plurality of data clusters to which the operational data output from the plurality of sensors belongs; and
communicating, by the EMS of the aircraft via a wireless channel, data indicative of valid operational states of the aircraft associated with the particular data cluster to a control system.

### Claim 2 (depends on 1)

The method according to claim 1, further comprising:
storing, to the EMS, data cluster definitions that specify the plurality of data clusters.

### Claim 3 (depends on 1)

The method according to claim 1, wherein clustering, via the multivariate window-based clustering algorithm, the extended training data further comprises:
clustering, via a Toeplitz Inverse Covariance-Based Clustering algorithm, the extended training data into the plurality of data clusters associated with the plurality of operational states of the aircraft.

### Claim 4 (depends on 3)

The method according to claim 3, wherein the Toeplitz Inverse Covariance-Based Clustering algorithm specifies a window size associated with an amount of time, wherein appending the first buffer data to the first training data further comprises:
appending first buffer data having a size equal to the window size to the first training data.

### Claim 5 (depends on 4)

The method according to claim 4, wherein clustering the extended training data into the plurality of data clusters associated with the plurality of operational states of the aircraft, comprises:
responsive to determining that a threshold amount of extended training data exists, clustering, by the EMS, the extended training data into the plurality of data clusters associated with the plurality of operational states of the aircraft.

### Claim 6 (depends on 1)

The method according to claim 1, further comprising:
responsive to the aircraft entering into a particular sequence of operational states, scheduling, by the control system, maintenance to be performed on the aircraft.

### Claim 7 (depends on 1)

The method according to claim 1, further comprising:
responsive to the aircraft entering into a particular sequence of operational states, communicating, by the EMS, the operational data associated with the plurality of sensors of the aircraft to the control system.

### Claim 8 (depends on 1)

The method according to claim 1, wherein clustering the extended training data into the plurality of data clusters associated with the plurality of operational states of the aircraft comprises:
clustering, by the EMS, the extended training data into a plurality of data clusters associated with the plurality of operational states of the aircraft, wherein the plurality of operational states indicate modes of operation of a component or subsystem of the aircraft.

### Claim 9

A system comprising:
a memory that stores instruction code; and
a processor in communication with the memory, wherein the instruction code is executable by the processor to perform operations comprising:
receiving first training data from a plurality of sensors of an aircraft over a first operating interval of the aircraft during a first flight, and second training data from the plurality of sensors of the aircraft over a second operating interval of the aircraft during a second flight;
appending first buffer data between the first training data and the second training data to provide extended training data;
determining whether the extended training data reaches a threshold extent of data;
appending second buffer data to the extended training data when the extended training data is less than the threshold extent of data and repeat training data collection from the plurality of sensors of the aircraft over a third operating interval;
based on determining that the extended training data reaches the threshold extent of data, clustering via execution of a multivariate window-based clustering algorithm to train the multivariate window-based clustering algorithm for the aircraft with training data that spans multiple flights for the aircraft, the extended training data into a plurality of data clusters associated with a plurality of operational states of the aircraft, such that values of sensor data from the plurality of sensors in the first training data and the second training data received during each operating interval are clustered according to being associated with a same operational state of the aircraft, wherein the plurality of operational states include valid operational states and invalid operational states and specifying values of the first buffer data so that the first buffer data clusters into an invalid operational state;
subsequent to clustering, receiving operational data output from the plurality of sensors of the aircraft over a fourth operating interval;
determining via execution of the trained multivariate window-based clustering algorithm, a particular data cluster of the plurality of data clusters to which the operational data output from the plurality of sensors belongs; and
communicating via a wireless channel, data indicative of valid operational states of the aircraft associated with the particular data cluster to a control system.

### Claim 10 (depends on 9)

The system according to claim 9, wherein the instruction code is executable by the processor to perform further operations comprising:
storing, to the memory, data cluster definitions that specify the plurality of data clusters.

### Claim 11 (depends on 9)

The system according to claim 9, wherein the multivariate window-based clustering algorithm corresponds to a Toeplitz Inverse Covariance-Based Clustering algorithm that specifies a window size and the plurality of operational states include valid operational states and invalid operational state, wherein in appending the first buffer data to the first training data, the instruction code is executable by the processor to perform operations comprising:
appending first buffer data having a size equal to the window size to the first training data.

### Claim 12 (depends on 11)

The system according to claim 11, wherein in clustering the extended training data into the plurality of data clusters associated with the plurality of operational states of the aircraft, the instruction code is executable by the processor to perform operations comprising:
responsive to determining that a threshold amount of extended training data exists, clustering the extended training data into the plurality of data clusters associated with the plurality of operational states of the aircraft.

### Claim 13 (depends on 9)

The system according to claim 9, wherein the instruction code is executable by the processor to perform further operations comprising:
responsive to the aircraft entering into a particular sequence of operational states, scheduling, by the control system, maintenance to be performed on the aircraft.

### Claim 14 (depends on 9)

The system according to claim 9, wherein the instruction code is executable by the processor to perform further operations comprising:
responsive to the aircraft entering into a particular sequence of operational states, communicating the operational data associated with the plurality of sensors of the aircraft to the control system.

### Claim 15 (depends on 9)

The system according to claim 9, wherein clustering the extended training data into the plurality of data clusters associated with the plurality of operational states of the aircraft, the instruction code is executable by the processor to perform operations comprising:
clustering the extended training data into a plurality of data clusters associated with the plurality of operational states of the aircraft, wherein the plurality of operational states indicate modes of operation of a component or subsystem of the aircraft.

### Claim 16

A non-transitory computer-readable medium having stored thereon instruction code, wherein the instruction code is executable by a processor of a computer to perform operations comprising:
receiving first training data from a plurality of sensors of an aircraft over a first operating interval of the aircraft during a first flight, and second training data from the plurality of sensors of the aircraft over a second operating interval of the aircraft during a second flight;
appending first buffer data between the first training data and the second training data to provide extended training data;
determining whether the extended training data reaches a threshold extent of data;
appending second buffer data to the extended training data when the extended training data is less than the threshold extent of data and repeat training data collection from the plurality of sensors of the aircraft over a third operating interval;
based on determining that the extended training data reaches the threshold extent of data, clustering via execution of a multivariate window-based clustering algorithm to train the multivariate window-based clustering algorithm for the aircraft with training data that spans multiple flights for the aircraft, the extended training data into a plurality of data clusters associated with a plurality of operational states of the aircraft, such that values of sensor data from the plurality of sensors in the first training data and the second training data received during each operating interval are clustered according to being associated with a same operational state of the aircraft, wherein the plurality of operational states include valid operational states and invalid operational states and specifying values of the first buffer data so that the first buffer data clusters into an invalid operational state;
subsequent to clustering, receiving operational data output from the plurality of sensors of the aircraft over a fourth operating interval;
determining via execution of the trained multivariate window-based clustering algorithm, a particular data cluster of the plurality of data clusters to which the operational data output from the plurality of sensors belongs; and
communicating via a wireless channel, data indicative of valid operational states of the aircraft associated with the particular data cluster to a control system.

### Claim 17 (depends on 16)

The non-transitory computer-readable medium of claim 16, wherein the instruction code is further executable by the processor to perform operations comprising:
storing data cluster definitions that specify the plurality of data clusters.

### Claim 18 (depends on 16)

The non-transitory computer-readable medium of claim 16, wherein the instruction code is further executable by the processor to perform operations comprising:
clustering, via a Toeplitz Inverse Covariance-Based Clustering algorithm, the extended training data into the plurality of data clusters associated with the plurality of operational states of the aircraft.

### Claim 19 (depends on 18)

The non-transitory computer-readable medium of claim 18, wherein the Toeplitz Inverse Covariance-Based Clustering algorithm specifies a window size associated with an amount of time, wherein appending the first buffer data to the first training data further comprises:
appending first buffer data having a size equal to the window size to the first training data.

### Claim 20 (depends on 16)

The non-transitory computer-readable medium of claim 16, wherein clustering the extended training data into the plurality of data clusters associated with the plurality of operational states of the aircraft comprises:
clustering the extended training data into a plurality of data clusters associated with the plurality of operational states of the aircraft, wherein the plurality of operational states indicate modes of operation of a component or subsystem of the aircraft.