Method Of Determining A Maneuver Performed By An Aircraft

• US 20110264310A1
• Filed: 02/18/2011
• Published: 10/27/2011
• Est. Priority Date: 04/21/2010
• Status: Active Grant

• Current Assignee: Sikorsky Aircraft Corporation (Martin Marietta Corporation)
• Original Assignee: Sikorsky Aircraft Corporation (Martin Marietta Corporation)
• Inventors: Cycon, James Peter, Sadegh, Payman, Bates, Preston R., Davis, Mark W.
• Granted Patent: US 8,744,651 B2
• Time in Patent Office: Days
• US Class Current: 701/14
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06
• CPC Class Codes: G07C 5/085 using electronic data carriers

Abstract

A method of determining a maneuver performed by an aircraft having sensors for monitoring motion data, the method including periodically sampling the sensors to electronically determine segments of motion data of the aircraft; aggregating sequences of the segments of the motion data; comparing the aggregated segments of motion data to models of particular maneuvers; and determining the maneuver performed by the aircraft.

Claims

1. A method of determining a maneuver performed by an aircraft having sensors for monitoring motion data, the method comprising:

periodically sampling the sensors to electronically determine segments of motion data of the aircraft;

aggregating sequences of the segments of the motion data;

comparing the aggregated segments of motion data to models of particular maneuvers; and

determining the maneuver performed by the aircraft.

2. The method of claim 1 wherein:

the segments of motion data are generated by a health and usage monitoring system (HUMS).

3. The method of claim 2 wherein:

the segments of motion data are generated by the HUMS are HUMS regime sequences.

4. The method of claim 3 wherein:

aggregating sequences of the segments of the motion data includes using a clustering algorithm to aggregate the HUMS regime sequences into a cluster.

5. The method of claim 4 wherein:

the cluster is associated with a persistence parameter.

6. The method of claim 5 further comprising:

calibrating the clustering algorithm to select a selected persistence parameter for the cluster.

7. The method of claim 6 wherein:

calibrating the clustering algorithm includes executing the clustering algorithm with a plurality of persistence parameters and selecting the selected persistence parameter from the plurality of persistence parameters.

8. The method of claim 7 wherein:

selecting the selected persistence parameter from the plurality of persistence parameters includes comparing cluster data for the plurality of persistence parameters to fleet parametric data.

9. The method of claim 8 wherein:

comparing cluster data for the plurality of persistence parameters to fleet parametric data includes comparing histograms of the cluster data for the plurality of persistence parameters to a histogram of the fleet parametric data.

10. The method of claim 5 further comprising:

analyzing the cluster to detect an over-clustering condition in which too many HUMS regime sequences are included in the cluster.

11. The method of claim 10 further comprising:

adjusting the persistence parameter for the cluster upon detecting the over-clustering condition.

12. The method of claim 5 further comprising:

analyzing the cluster to detect an under-clustering condition in which too few HUMS regime sequences are included in the cluster.

13. The method of claim 12 further comprising:

adjusting the persistence parameter for the cluster upon detecting the under-clustering condition.

14. The method of claim 4 further comprising:

analyzing the cluster to detect a miss condition in which the cluster does not represent the maneuver.

15. The method of claim 14 further comprising:

creating a new cluster definition upon detecting a miss condition.

16. The method of claim 4 further comprising:

developing a reliability factor for the cluster.