Methods for detecting one or more aircraft anomalies and devices thereof

• US 9,865,101 B2
• Filed: 12/28/2015
• Issued: 01/09/2018
• Est. Priority Date: 10/30/2015
• Status: Active Grant

• Current Assignee: Wipro Limited
• Original Assignee: Wipro Limited
• Inventors: Amirapu, Raja Sekhar
• Primary Examiner(s): FREJD, RUSSELL WARREN
• Application Number:
  US14/981,334
• Publication Number:
  US 20170124782A1
• Time in Patent Office: 743 Days
• CPC Class Codes: B64D 2045/0085 Devices for aircraft health monitoring, e.g. monitoring flutter or vibration
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• CPC Class Codes: G05B 23/0281 Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis
• CPC Class Codes: G06F 16/2471 Distributed queries
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005

Abstract

Methods, devices, and non-transitory computer readable media that detect an anomaly in an aircraft include obtaining aircraft flight data from multiple aircraft sensor devices. The obtained aircraft flight data is clustered into two or more data groups. A distance between the clustered aircraft flight data in at least one of the two or more data groups associated with a part of the aircraft and stored baseline flight data for the part of the aircraft is determined. A statistical model analysis is executed on the determined distance to detect any anomaly with the part of the aircraft.

Claims

1. A method for detecting an anomaly in an aircraft, the method comprising:

obtaining, by a big data analytic computing device, aircraft flight data from multiple aircraft sensor devices;

clustering, by the big data analytic computing device, the obtained aircraft flight data into two or more data groups;

computing, by the big data analytic computing device, a distance between the clustered aircraft flight data in at least one of the two or more data groups associated with a part of the aircraft and stored baseline flight data for the part of the aircraft, wherein the distance is at least one of a Euclidean distance or a dynamic time warping distance; and

executing, by the big data analytic computing device, a statistical model analysis on the determined distance to detect an anomaly with the part of the aircraft.

2. The method as set forth in claim 1 wherein the clustering the obtained aircraft flight data further comprises grouping, by the big data analytic computing device, the obtained aircraft flight data into a resilient distributed dataset.

3. The method as set forth in claim 1 wherein the executing further comprises executing, by the big data analytic computing device, at least one of a regression model, a Markovian model, or a compression model on the determined distance to detect the anomaly with the part of the aircraft.

4. The method as set forth in claim 1 further comprising computing, by the big data analytic computing device, at least one of a mean time between failure, mean time between critical failure, or mean time to repair for the part of the aircraft based on the detected anomaly with the part of the aircraft.

5. The method as set forth in claim 1 further comprising generating and providing, by the big data analytic computing device, a graphical display of the detected anomaly with the part of the aircraft.

6. A big data analytic computing device, comprising:

one or more processors;

a memory coupled to the one or more processors which are configured to execute programmed instructions comprising and stored in the memory to;

obtain aircraft flight data from multiple aircraft sensor devices;

cluster the obtained aircraft flight data into two or more data groups;

compute a distance between the clustered aircraft flight data in at least one of the two or more data groups associated with a part of the aircraft and stored baseline flight data for the part of the aircraft, wherein the distance is at least one of a Euclidean distance or a dynamic time warping distance; and

execute a statistical model analysis on the determined distance to detect an anomaly with the part of the aircraft.

7. The device as set forth in claim 6 wherein the processors are further configured to execute one or more additional programmed instructions comprising and stored in the memory to group the obtained aircraft flight data into a resilient distributed dataset.

8. The device as set forth in claim 6 wherein the processors are further configured to execute one or more additional programmed instructions comprising and stored in the memory to execute at least one of a regression model, a Markovian model, or a compression model on the determined distance to detect the anomaly with the part of the aircraft.

9. The device as set forth in claim 6 wherein the processors are further configured to execute one or more additional programmed instructions comprising and stored in the memory to compute at least one of a mean time between failure, mean time between critical failure, or mean time to repair for the part of the aircraft based on the detected anomaly with the part of the aircraft.

10. The device as set forth in claim 6 wherein the processors are further configured to execute one or more additional programmed instructions comprising and stored in the memory to generate and provide a graphical display of the detected anomaly with the part of the aircraft.

11. A non-transitory computer readable medium comprising instructions stored thereon for improving product performance, which when executed by at least one processor, cause the processor to perform steps comprising:

obtaining aircraft flight data from multiple aircraft sensor devices;

clustering the obtained aircraft flight data into two or more data groups;

computing a distance between the clustered aircraft flight data in at least one of the two or more data groups associated with a part of the aircraft and stored baseline flight data for the part of the aircraft, wherein the distance is at least one of a Euclidean distance or a dynamic time warping distance; and

executing a statistical model analysis on the determined distance to detect an anomaly with the part of the aircraft.

12. The non-transitory computer readable medium as claimed in claim 11,

further comprising one or more additional programmed instructions, which when executed by the processor, further cause the processor to perform one or more additional steps comprising grouping the obtained aircraft flight data into a resilient distributed dataset.

13. The non-transitory computer readable medium as claimed in claim 11,

further comprising one or more additional programmed instructions, which when executed by the processor, further cause the processor to perform one or more additional steps comprising executing at least one of a regression model, a Markovian model, or a compression model on the determined distance to detect the anomaly with the part of the aircraft.

14. The non-transitory computer readable medium as claimed in claim 11,

further comprising one or more additional programmed instructions, which when executed by the processor, further cause the processor to perform one or more additional steps comprising computing at least one of a mean time between failure, mean time between critical failure, or mean time to repair for the part of the aircraft based on the detected anomaly with the part of the aircraft.

15. The non-transitory computer readable medium as claimed in claim 11, further comprising one or more additional programmed instructions, which when executed by the processor, further cause the processor to perform one or more additional steps comprising generating and providing a graphical display of the detected anomaly with the part of the aircraft.