Automated fault isolation of flight control surfaces and damage detection of aircraft through non-contact measurement

Patent metadata

Field	Value
Publication number	US-11299294-B2
Application number	US-201916429663-A
Country	US
Kind code	B2
Filing date	Jun 3, 2019
Priority date	Jun 6, 2018
Publication date	Apr 12, 2022
Grant date	Apr 12, 2022

Abstract

Systems, computer-implemented methods and/or computer program products that facilitate automating fault isolation of flight control surfaces and damage detection of aircraft are provided. In one embodiment, a system 100 utilizes a processor 102 that executes computer implemented components stored in memory 104. One or more sensors 106 monitor wing and flight control surface from one or more vantage points to obtain full coverage of the wing. A machine vision component 108 assesses condition of the wing and the flight control surface based on results of the one or more sensors 106. A spectrographic component 110 detects leaks by monitoring airflow for traces of fuel or hydraulic fluid.

First claim

What is claimed:

1. A system, comprising:
a memory that stores computer executable instructions; and
a processor, operably coupled to the memory, and that executes the computer executable instructions stored in the memory, which causes the processor to:
monitor, via execution of a plurality of sensors, a wing of an aircraft and a flight control surface of the wing, wherein the plurality of sensors are positioned at a plurality of respective vantage points on the aircraft to obtain full coverage of the wing;
assess, via execution of a machine learning algorithm, a condition of the wing and the flight control surface, wherein the machine learning algorithm receives as input data that is produced by the plurality of sensors; and
detect, via execution of a spectrometer, fuel leaks or hydraulic fluid leaks, wherein the spectrometer is positioned on a downstream outer surface of the wing and is configured to monitor airflow for traces of fuel or hydraulic fluid.

2. The system of claim 1 , wherein the machine learning algorithm uses inputs from the flight control surface to compare actual deflection and demanded deflection to identify location of failure as a function of deviation of the actual deflection from the demanded deflection.

3. The system of claim 1 , wherein the plurality of sensors monitor landing gear.

4. The system of claim 1 , wherein the plurality of sensors are cameras that employ infrared for vision analysis in low visibility conditions.

5. The system of claim 1 , wherein the plurality of sensors utilize at least one of: lidar, radar or ultrasound.

6. The system of claim 1 , wherein the plurality of sensors are electromechanical sensors or digital sensors.

7. The system of claim 1 , wherein the plurality of sensors are acoustic sensors that monitor sound signature of the wing and the flight control surface.

8. The system of claim 1 , wherein the machine learning algorithm classifies type of damage and identifies cause of damage.

9. The system of claim 8 , wherein the machine learning algorithm determines location of the damage based on sensor data from the plurality of sensors.

10. The system of claim 9 , wherein the computer executable instructions are further executable to cause the processor to notify the damage to flight crew or cabin crew.

11. The system of claim 1 , wherein the machine learning algorithm transmits assessment data into a cloud to enhance learning capabilities.

12. A computer-implemented method, comprising:
monitoring, by a system operatively coupled to a processor and via execution of a plurality of sensors, a wing of an aircraft and a flight control surface of the wing, wherein the plurality of sensors are positioned at a plurality of respective vantage points on the aircraft to obtain full coverage of the wing;
assessing, by the system and via execution of a machine learning algorithm, a condition of the wing and the flight control surface, wherein the machine learning algorithm receives as input data that is produced by the plurality of sensors; and
detecting, by the system and via execution of a spectrometer, fuel leaks or hydraulic fluid leaks, wherein the spectrometer is positioned on a downstream outer surface of the wing and is configured to monitor airflow for traces of fuel or hydraulic fluid.

13. The computer-implemented method of claim 12 , wherein the assessing the condition of the wing and the flight control surface comprises classifying type of damage and identifying cause of damage.

14. The computer-implemented method of claim 13 , wherein the assessing the condition of the wing and the flight control surface comprises determining location of the damage based on sensor data from the plurality of sensors.

15. The computer-implemented method of claim 12 , further comprising notifying the damage to flight crew or cabin crew.

16. The computer-implemented method of claim 12 , wherein the assessing the condition of the wing and the flight control surface comprises transmitting assessment data into a cloud to enhance learning capabilities.

17. A computer program product for facilitating automating fault isolation of flight control surfaces and damage detection of aircraft, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
monitor, via execution of a plurality of sensors, a wing of an aircraft and a flight control surface of the wing, wherein the plurality of sensors are positioned at a plurality of respective vantage points on the aircraft to obtain full coverage of the wing;
assess, via execution of a machine learning algorithm, a condition of the wing and the flight control surface, wherein the machine learning algorithm receives as input data produced by the plurality of sensors; and
detect, via execution of a spectrometer, fuel leaks or hydraulic fluid leaks, wherein the spectrometer is positioned on a downstream outer surface of the wing and is configured to monitor airflow for traces of fuel or hydraulic fluid.

18. The computer program product of claim 17 , wherein the program instructions are further executable to cause the processor to:
classify type of damage and identify cause of damage.

19. The computer program product of claim 18 , wherein the program instructions are further executable to cause the processor to:
determine location of the damage based on sensor data from the plurality of sensors.

20. The computer program product of claim 17 , wherein the program instructions are further executable to cause the processor to:
transmit assessment data into a cloud to enhance learning capabilities.

Assignees

• Ge Aviat Systems Ltd

Inventors

• Parker Edward
• Schwindt Stefan Alexander

Classifications

• B64D47/08

  Arrangements of cameras · CPC title

• B64F5/60 Primary

  Testing or inspecting aircraft components or systems · CPC title

• G01M3/202 Primary

  using mass spectrometer detection systems · CPC title

• B64D2045/0085

  Devices for aircraft health monitoring, e.g. monitoring flutter or vibration · CPC title

• G01N21/88

  Investigating the presence of flaws or contamination · CPC title