Reporting and prioritizing faults for aircraft downtime reduction

Patent metadata

Field	Value
Publication number	US-10643187-B2
Application number	US-201715711262-A
Country	US
Kind code	B2
Filing date	Sep 21, 2017
Priority date	Jun 9, 2017
Publication date	May 5, 2020
Grant date	May 5, 2020

Abstract

Disclosed is a computer-implemented method for maintenance planning for an aircraft. The method includes retrieving, via a processor, a data transmission comprising a plurality of component faults from an aircraft processor while the aircraft is in flight. The processor executes, using a prediction engine, a predictive fault list based on the component faults. The predictive fault list includes a plurality of weighted predictions of authentic component faults and nuisance component faults. The processor prioritizes the weighted predictions of authentic component faults, and generates a maintenance checklist prioritized based on the weighted prediction of authentic component faults. The processor then outputs the prioritized maintenance checklist on an operatively connected maintenance planning device.

First claim

What is claimed is:

1. A computer-implemented method for maintenance planning for an aircraft, the method comprising:
measuring, using one or more sensors, conditions of the aircraft;
retrieving, via a processor, a data transmission comprising a plurality of component faults from an aircraft processor while the aircraft is in flight based at least in part on measurements from the one or more sensors;
constructing, via the processor executing a prediction engine, a predictive fault list based on the component faults, the predictive fault list having a plurality of weighted predictions of authentic component faults and nuisance component faults;
prioritizing, via the processor, the weighted predictions of authentic component faults;
generating, via the processor, a maintenance checklist prioritized based on the weighted prediction of authentic component faults; and
outputting, via an output processor, the prioritized maintenance checklist on an operatively connected maintenance planning device;
wherein constructing the predictive fault list comprises:
retrieving, via the processor, a part failure history for each sensor indicated in the plurality of component faults from the aircraft processor while the aircraft is in flight; and
evaluating, based on the part failure history, for each sensor associated with a particular sensor fault in the plurality of component faults from the aircraft processor, a relationship between 1) an aircraft flight path, 2) the particular sensor fault, and 3) a prediction hit or miss rate for the sensor associated with the sensor fault.

2. The computer-implemented method of claim 1 , further comprising obtaining, via the processor, a prediction accuracy report indicative of an accuracy for each of the weighted predictions of authentic component faults and nuisance component faults;
generating, via the processor, an accuracy table indicative of a relative accuracy for each of the weighted predictions; and
configuring, via the processor, the prediction engine based on the accuracy table.

3. The computer-implemented method of claim 1 , further comprising predicting, for each of the plurality of component faults, the weighted prediction of authentic component faults based on the relationship between the aircraft flight path, the sensor fault, and the prediction hit or miss rate for the sensor associated with the sensor fault.

4. The computer-implemented method of claim 3 , wherein the aircraft flight path comprises a predetermined flight leg unique to the aircraft flight path.

5. The computer-implemented method of claim 4 , wherein the aircraft flight path comprises one or more of a flight parameters including but not limited to flight vector, an engine speed, and an altitude.

6. A system for aircraft maintenance planning comprising:
one or more sensors configured to measure conditions of an aircraft;
a processor configured to:
retrieve a data transmission comprising a plurality of component faults from the aircraft processor while the aircraft is in flight based at least in part on measurement from the one or more sensors;
construct, via a prediction engine, a predictive fault list based on the component faults, the predictive fault list having a plurality of weighted predictions of authentic component faults and nuisance component faults;
prioritize, via the prediction engine, the weighted predictions of authentic component faults;
generate a maintenance checklist prioritized based on the weighted prediction of authentic component faults; and
output the prioritized maintenance checklist on an operatively connected maintenance planning device;
wherein constructing the predictive fault list comprises:
retrieving, via the processor, a part failure history for each sensor indicated in the plurality of component faults from the aircraft processor while the aircraft is in flight; and
evaluating, based on the part failure history, for each sensor associated with a particular sensor fault in the plurality of component faults from the aircraft processor, a relationship between 1) an aircraft flight path, 2) the particular sensor fault, and 3) a prediction hit or miss rate for the sensor associated with the sensor fault.

7. The system of claim 6 , further comprising predicting, for each of the plurality of component faults, the weighted prediction of authentic component faults based on the relationship between the aircraft flight path, the sensor fault, and the prediction hit or miss rate for the sensor associated with the sensor fault.

8. The system of claim 7 , wherein the aircraft flight path comprises a predetermined flight leg unique to the aircraft flight path.

9. The system of claim 8 , wherein the aircraft flight path comprises one or more of a flight data including but not limited to flight vector, an engine speed, and an altitude.

10. A non-transitory computer readable medium comprising a computer program product configured to, when executed on a processor, perform a method for aircraft maintenance planning comprising:
one or more sensors configured to measure conditions of an aircraft,
retrieving, via the processor, a data transmission comprising a plurality of component faults from an aircraft processor while the aircraft is in flight based at least in part on measurements from the one or more sensors;
constructing, via the processor executing a prediction engine, a predictive fault list based on the component faults, the predictive fault list having a plurality of weighted predictions of authentic component faults and nuisance component faults;
prioritizing, via the processor, the weighted predictions of authentic component faults;
generating, via the processor, a maintenance checklist prioritized based on the weighted prediction of authentic component faults; and
outputting, via an output processor, the prioritized maintenance checklist on an operatively connected maintenance planning device;
wherein constructing the predictive fault list comprises:
retrieving, via the processor, a part failure history for each sensor indicated in the plurality of component faults from the aircraft processor while the aircraft is in flight; and
evaluating, based on the part failure history, for each sensor associated with a particular sensor fault in the plurality of component faults from the aircraft processor, a relationship between 1) an aircraft flight path, 2) the particular sensor fault, and 3) a prediction hit or miss rate for the sensor associated with the sensor fault.

11. The non-transitory computer readable medium of claim 10 , further comprising obtaining, via the processor, a prediction accuracy report indicative of an accuracy for each of the weighted predictions of authentic component faults and nuisance component faults;
generating, via the processor, an accuracy table indicative of a relative accuracy for each of the weighted predictions; and
configuring, via the processor, the prediction engine based on the accuracy table.

12. The non-transitory computer readable medium of claim 10 , further comprising predicting, for each of the plurality of component faults, the weighted prediction of authentic component faults based on the relationship between the aircraft flight path, the sensor fault, and the prediction hit or miss rate for the sensor associated with the sensor fault.

13. The non-transitory computer readable medium of claim 12 , wherein the aircraft flight path comprises a predetermined flight leg unique to the aircraft flight path.

14. The non-transitory computer readable medium of claim 13 , wherein the aircraft flight path comprises one or more of a flight vector, a

Assignees

• Kiddie Tech Inc
• Kidde Tech Inc

Inventors

• Barkat Mohammad Adil
• Karan Vivek
• Nandakumar Pradeep Kumar

Classifications

• G06N5/04

  Inference or reasoning models · CPC title

• G07C5/0841

  Registering performance data (recording measured values G01D; information storage G11B) · CPC title

• G06N20/00

  Machine learning · CPC title

• G06Q10/06316

  Sequencing of tasks or work · CPC title

• B64F5/40

  Maintaining or repairing aircraft · CPC title