Par system for analyzing aircraft flight data

• US 6,480,770 B1
• Filed: 03/31/2000
• Issued: 11/12/2002
• Est. Priority Date: 04/01/1999
• Status: Expired due to Fees

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Wischmeyer, Ed
• Primary Examiner(s): Cuchlinski, Jr., William A.
• Assistant Examiner(s): Marc-Coleman, Marthe Y.
• Application Number:
  US09/541,474
• Time in Patent Office: 956 Days
• Field of Search: 701/29, 701/13, 701/14, 701/35, 701/15, 701/16
• US Class Current: 701/29
• CPC Class Codes: B64D 2045/008 Devices for detecting or indicating hard landing
• CPC Class Codes: B64D 45/00 Aircraft indicators or protectors not otherwise provided for camouflage F41H3/00

Abstract

A system for analyzing aircraft data includes the steps of identifying a domain comprising sets of data, calculating ranges of typical values for components of the sets of data within the domain, and determining atypical components for each set of data within the sets of data based on the range of typical values calculated in the calculating step. A pre-cursor pattern to an exceedance event is determined by (a) identifying exceedance sets of data within the domain corresponding to an exceedance event, and (b) identifying common atypicalities of the exceedance sets of data. Mitigating factors for an exceedance event are determined by (a) identifying non-event sets of data within the domain in which atypicalities resembling the pre-cursor pattern exist but in which the exceedance event does not occur, and (b) identifying common atypicalities of the non-event sets of data as the mitigating factors. The consequences of an event are determined by (a) identifying event sets of data corresponding to the event, and (b) comparing the atypicalities of the event sets of data with the typical values, where the consequences are the common atypicalities of the event sets of data. Mitigating factors for the consequences of the event are determined by (a) chronologically sorting the common atypicalities of the event sets of data, and (b) identifying pairings of atypicalities comprising a mitigation and a consequence, where a mitigating factor is a mitigation that occurred when a later consequence usually did not occur or is a mitigation that did not occur when a later consequence usually did occur.

Claims

1. A method for analyzing aircraft data, comprising the steps of:

identifying a domain comprising sets of data;

calculating, from said sets of data, ranges of typical values for components of the sets of data within the domain; and

determining atypical components for each set of data within the sets of data based on the typical values calculated in said calculating step.

2. The method for analyzing aircraft data according to claim 1, further comprising the step of:

determining atypical sets of data by (a) calculating a weight of the atypical components for each set of data within the sets of data, and (b) comparing the weight of each set of data to a distribution of the weights of all of the sets of data within the domain,

wherein a set of data is atypical if its weight is high with respect to the distribution of the weights of the sets of data within the domain.

3. The method for analyzing aircraft data according to claim 1, wherein the domain is limited by identifying specific sets of data within the domain that relate to a specific event.

4. The method for analyzing aircraft data according to claim 3, further comprising the step of:

determining a pre-cursor pattern to an exceedance event by (a) identifying exceedance sets of data within the domain corresponding to an exceedance event, and (b) identifying common atypicalities of the exceedance sets of data,

wherein all components of the exceedance sets of data occurring after the exceedance event are ignored.

5. The method for analyzing aircraft data according to claim 4, further comprising the step of:

determining the risk of the occurrence of the exceedance event for a risky set of data within the domain by comparing atypicalities of the risky set of data with the pre-cursor pattern determined in said step of determining a pre-cursor pattern,

wherein the risk is proportional to the amount of correlation between the atypicalities of the risky set of data and the pre-cursor pattern.

6. The method for analyzing aircraft data according to claim 4, further comprising the step of:

determining mitigating factors for an exceedance event by (a) identifying non-event sets of data within the domain in which atypicalities resembling the pre-cursor pattern exist but in which the exceedance event does not occur, and (b) identifying common atypicalities of the non-event sets of data as the mitigating factors.

7. The method for analyzing aircraft data according to claim 6, wherein atypicalities of the pre-cursor pattern are ignored.

8. The method for analyzing aircraft data according to claim 3, further comprising the step of:

determining the consequences of an event by (a) identifying event sets of data corresponding to the event, and (b) comparing the atypicalities of the event sets of data with the typical values,

wherein all components of the event sets of data occurring before the event are ignored, and

wherein the consequences are the common atypicalities of the event sets of data.

9. The method for analyzing aircraft data according to claim 8, further comprising the step of:

determining mitigating factors for the consequences of the event by (a) chronologically sorting the common atypicalities of the event sets of data, and (b) identifying pairings of atypicalities comprising a mitigation and a consequence,

wherein a mitigating factor is a mitigation that occurred when a later consequence did not occur in a majority of the flights or is a mitigation that did not occur when a later consequence did occur in a majority of the flights.

10. A computer readable storage device containing computer executable code for a system for analyzing aircraft data, said device including code for executing steps comprising:

identifying a domain comprising sets of data;

calculating, from said sets of data, ranges of typical values for components of the sets of data within the domain; and

determining atypical components for each set of data within the sets of data based on the typical values calculated in said calculating step.

11. A computer readable storage device according to claim 10, said device including code for further executing the step comprising:

determining atypical sets of data by (a) calculating a weight of the atypical components for each set of data within the sets of data, and (b) comparing the weight of each set of data to a distribution of the weights of all of the sets of data within the domain,

wherein a set of data is atypical if its weight is high with respect to the distribution of the weights of the sets of data within the domain.

12. A computer readable storage device according to claim 10, wherein the domain is limited by identifying specific sets of data within the domain that relate to a specific event.

13. A computer readable storage device according to claim 12, said device including code for further executing the step comprising:

determining a pre-cursor pattern to an exceedance event by (a) identifying exceedance sets of data within the domain corresponding to an exceedance event, and (b) identifying common atypicalities of the exceedance sets of data,

wherein all components of the exceedance sets of data occurring after the exceedance event are ignored.

14. A computer readable storage device according to claim 13, said device including code for further executing the step comprising:

determining the risk of the occurrence of the exceedance event for a risky set of data within the domain by comparing atypicalities of the risky set of data with the pre-cursor pattern determined in said step of determining a pre-cursor pattern,

wherein the risk is proportional to the amount of correlation between the atypicalities of the risky set of data and the pre-cursor pattern.

15. A computer readable storage device according to claim 13, said device including code for further executing the step comprising:

determining mitigating factors for an exceedance event by (a) identifying non-event sets of data within the domain in which atypicalities resembling the pre-cursor pattern exist but in which the exceedance event does not occur, and (b) identifying common atypicalities of the non-event sets of data as the mitigating factors.

16. A computer readable storage device according to claim 15, wherein atypicalities of the pre-cursor pattern are ignored.

17. A computer readable storage device according to claim 12 said device including code for further executing the step comprising:

determining the consequences of an event by (a) identifying event sets of data corresponding to the event, and (b) comparing the atypicalities of the event sets of data with the typical values,

wherein all components of the event sets of data occurring before the event are ignored, and

wherein the consequences are the common atypicalities of the event sets of data.

18. A computer readable storage device according to claim 17, said device including code for further executing the step comprising:

determining mitigating factors for the consequences of the event by (a) chronologically sorting the common atypicalities of the event sets of data, and (b) identifying pairings of atypicalities comprising a mitigation and a consequence,

wherein a mitigating factor is a mitigation that occurred when a later consequence did not occur in a majority of the flights or is a mitigation that did not occur when a later consequence did occur in a majority of the flights.

19. Computer executable code stored on a computer readable medium implementing a system for analyzing aircraft data, said code for executing the steps comprising:

identifying a domain comprising sets of data;

calculating from said sets of data, ranges of typical values for components of the sets of data within the domain; and

determining atypical components for each set of data within the sets of data based on the typical values calculated in said calculating step.

20. Computer executable code stored on a computer readable medium according to claim 19, said code for further executing the step comprising:

determining atypical sets of data by (a) calculating a weight of the atypical components for each set of data within the sets of data, and (b) comparing the weight of each set of data to a distribution of the weights of all of the sets of data within the domain,

wherein a set of data is atypical if its weight is high with respect to the distribution of the weights of the sets of data within the domain.

21. Computer executable code stored on a computer readable medium according to claim 19, wherein the domain is limited by identifying specific sets of data within the domain that relate to a specific event.

22. Computer executable code stored on a computer readable medium according to claim 21, said code for further executing the step comprising:

determining a pre-cursor pattern to an exceedance event by (a) identifying exceedance sets of data within the domain corresponding to an exceedance event, and (b) identifying common atypicalities of the exceedance sets of data,

wherein all components of the exceedance sets of data occurring after the exceedance event are ignored.

23. Computer executable code stored on a computer readable medium according to claim 22, said code for further executing the step comprising:

determining the risk of the occurrence of the exceedance event for a risky set of data within the domain by comparing atypicalities of the risky set of data with the pre-cursor pattern determined in said step of determining a pre-cursor pattern,

wherein the risk is proportional to the amount of correlation between the atypicalities of the risky set of data and the pre-cursor pattern.

24. Computer executable code stored on a computer readable medium according to claim 22, said code for further executing the step comprising:

determining mitigating factors for an exceedance event by (a) identifying non-event sets of data within the domain in which atypicalities resembling the pre-cursor pattern exist but in which the exceedance event does not occur, and (b) identifying common atypicalities of the non-event sets of data as the mitigating factors.

25. Computer executable code stored on a computer readable medium according to claim 24, wherein atypicalities of the pre-cursor pattern are ignored.

26. Computer executable code stored on a computer readable medium according to claim 21, said code for further executing the step comprising:

determining the consequences of an event by (a) identifying event sets of data corresponding to the event, and (b) comparing the atypicalities of the event sets of data with the typical values,

wherein all components of the event sets of data occurring before the event are ignored, and

wherein the consequences are the common atypicalities of the event sets of data.

27. Computer executable code stored on a computer readable medium according to claim 26, said code for further executing the step comprising:

determining mitigating factors for the consequences of the event by (a) chronologically sorting the common atypicalities of the event sets of data, and (b) identifying pairings of atypicalities comprising a mitigation and a consequence,

wherein a mitigating factor is a mitigation that occurred when a later consequence did not occur in a majority of the flights or is a mitigation that did not occur when a later consequence did occur in a majority of the flights.

28. A workstation for analyzing aircraft data stored on a memory, comprising:

a processor including a system for analyzing aircraft data, said system comprising;

means for identifying a domain comprising sets of data;

means for calculating, from said sets of data, ranges of typical values for components of the sets of data within the domain; and

means for determining atypical components for each set of data within the sets of data based on the typical values calculated in said calculating means.

29. A workstation for analyzing aircraft data according to claim 28, said system further comprising:

means for determining atypical sets of data by (a) calculating a weight of the atypical components for each set of data within the sets of data, and (b) comparing the weight of each set of data to a distribution of the weights of all of the sets of data within the domain,

wherein a set of data is atypical if its weight is high with respect to the distribution of the weights of the sets of data within the domain.

30. A workstation for analyzing aircraft data according to claim 28, further comprising identifying means for limiting the domain to specific sets of data within the domain that relate to a specific event.

31. A workstation for analyzing aircraft data according to claim 30, said system further comprising:

means for determining a pre-cursor pattern to an exceedance event by (a) identifying exceedance sets of data within the domain corresponding to an exceedance event, and (b) identifying common atypicalities of the exceedance sets of data,

wherein all components of the exceedance sets of data occurring after the exceedance event are ignored.

32. A workstation for analyzing aircraft data according to claim 31, said system further comprising:

means for determining the risk of the occurrence of the exceedance event for a risky set of data within the domain by comparing atypicalities of the risky set of data with the pre-cursor pattern determined in said step of determining a pre-cursor pattern,

wherein the risk is proportional to the amount of correlation between the atypicalities of the risky set of data and the pre-cursor pattern.

33. A workstation for analyzing aircraft data according to claim 31, said system further comprising:

means for determining mitigating factors for an exceedance event by (a) identifying non-event sets of data within the domain in which atypicalities resembling the pre-cursor pattern exist but in which the exceedance event does not occur, and (b) identifying common atypicalities of the non-event sets of data as the mitigating factors.

34. A workstation for analyzing aircraft data according to claim 33, wherein said means for determining mitigating factors further includes (c) ignoring atypicalities of the pre-cursor pattern.

35. A workstation for analyzing aircraft data according to claim 30, said system further comprising:

means for determining the consequences of an event by (a) identifying event sets of data corresponding to the event, and (b) comparing the atypicalities of the event sets of data with the typical values,

wherein all components of the event sets of data occurring before the event are ignored, and

wherein the consequences are the common atypicalities of the event sets of data.

36. A workstation for analyzing aircraft data according to claim 35, said system further comprising:

means for determining mitigating factors for the consequences of the event by (a) chronologically sorting the common atypicalities of the event sets of data, and (b) identifying pairings of atypicalities comprising a mitigation and a consequence,

wherein a mitigating factor is a mitigation that occurred when a later consequence did not occur in a majority of the flights or is a mitigation that did not occur when a later consequence did occur in a majority of the flights.