Identification of atypical flight patterns

• US 6,937,924 B1
• Filed: 05/21/2004
• Issued: 08/30/2005
• Est. Priority Date: 05/21/2004
• Status: Expired due to Fees

• Current Assignee: United States of America as represented by the Administrator of NASA, USA As Represented By The Administrator Of The NASA
• Original Assignee: United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration
• Inventors: Robin, Daniel E., Mosbrucker, Chris, Andrei, Adi L., White, Amanda M., Whitney, Paul D., Rosenthal, Loren J., Chidester, Thomas R., Romanowski, Timothy P., Willse, Alan R., Statler, Irving C., Cooley, Scott K., Amidan, Brett G., Prothero, Jason W., Jay, Joseph Griffith, Ferryman, Thomas A., Prothero, Gary L., Lawrence, Robert E., Lynch, Robert E.
• Primary Examiner(s): Black, Thomas G.
• Assistant Examiner(s): Tran, Dalena
• Application Number:
  US10/857,376
• Time in Patent Office: 466 Days
• Field of Search: 701/14, 701/214, 701/207, 701/213, 701/3, 701/4, 701/16, 701/200, 244/184, 244/203, 244/158.R, 244/75.R, 731/78.R, 734/88
• US Class Current: 701/14
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083

Abstract

Method and system for analyzing aircraft data, including multiple selected flight parameters for a selected phase of a selected flight, and for determining when the selected phase of the selected flight is atypical, when compared with corresponding data for the same phase for other similar flights. A flight signature is computed using continuous-valued and discrete-valued flight parameters for the selected flight parameters and is optionally compared with a statistical distribution of other observed flight signatures, yielding atypicality scores for the same phase for other similar flights. A cluster analysis is optionally applied to the flight signatures to define an optimal collection of clusters. A level of atypicality for a selected flight is estimated, based upon an index associated with the cluster analysis.

Claims

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

(i) receiving flight data for measurements of each of P selected parameters {m(t;

k;

q)} (k=1, . . . , P) at each of N selected times (t=t_n) (n=n0, . . . , n0+N−

1;

N≧

2) for one or more selected flights (q) of one or more aircraft;

(ii) for each continuous-valued parameter p(t;

k1) of each flight, numbered k1=1, . . . , K1 (K1≧

0), and for a selected sequence of the times t=t_n (n=n₀, n0+1, . . . , n=n0+N−

1, providing a polynomial approximation p(t;

k1;

app)=a (t_n0;

k1)+b (t_n0;

k1)·

(t−

t_n0)+c(t_n0;

k1).(t−

t_n0)²+e(t_n0;

k1), where e(t_n0;

k1) is an error term, whose sum of the squares d(t_n0;

k1)=(N−

3)<sup>−

1</sup>*Σ

e(t_n;

k1)², is minimized by the choice of the terms a(t_n0;

k1), b (t_n0;

k1) and c(t_n0;

k1);

(iii) forming vectors A={a(t_n0;

k1)}_n0, B={b(t_n0;

k1)}_n0,C={c(t_n0;

k1)}_n0, and D={d(t_n0;

k1)}_n0, forming an M1×

1 vector E1 including a first order statistic m1(v), a second order statistic m2(v), a minimum value min(v) and a maximum value max(v) for each of the vectors v=A, v=B, v=C and v=D;

(iv) for each discrete-valued parameter, numbered k2=1 . . . , K2 (K2≧

0) and having L(k2) discrete values, and for the selected sequence of times, forming an L(k2)×

L(k2) matrix whose entries are the number of transitions between any two of the L(k2) discrete values of this parameter, dividing each of the original diagonal entries by a sum of the original diagonal entries of the L(k2)×

L(k2) matrix to form a modified L(k2)×

L(k2) matrix, and forming an L×

1 vector E2 of entries from the modified L(k2)×

L(k2) matrices, where L is the sum of the values L(k2)²;

(v) forming an M×

1 data vector E with entries including m1(v), m2(v), min(v) and max(v) for each of the vectors v=A, v=B, v=C and v=D, and including the entries of the modified L×

1 vector, where M=M1+L;

(vi) computing a covariance matrix F=cov(E);

(vii) computing eigenvalues, λ

=λ

1, λ

2, . . . , λ

M, for an equation F·

V(λ

)=λ

V(λ

), where λ

1≧

λ

2≧

. . . ≧

λ

M; and

(viii) computing a transformed matrix G=DM·

F, where DM is a selected data matrix.

2. The method of claim 1, further comprising:

providing at least one sub-sequence of at least one of said values m(t_n;

k1q), and computing a selected linear combination of one or more of said values m(t_n;

k1q) in the sub-sequence;

comparing the computed linear combination of said values with a reference range of values for the computed linear combination; and

when the computed linear combination of said values does not lie within the reference range, interpreting this condition as indicating that at least one of said parameter values in the sub-sequence is unacceptable.

3. The method of claim 2, further comprising:

when said computed linear combination of said values lies within said reference range, interpreting this condition as indicating that said values in said sub-sequence are acceptable.

4. The method of claim 1, further comprising computing an atypicality score A_q, defined as $A_q=(1/(M^{\prime }$

-3))⁢

∑

j=1M′

⁢

⁢

(Gqj)2/λ

j′

,

where G_qj is an entry in said matrix G and {λ

′

1, λ

2, . . . , λ

′

M′

} is a selected subset of said eigenvalues {λ

1, λ

2, . . . , λ

′

M′

}, with M′

≦

M.

5. The method of claim 4, further comprising comparing said computed atypicality score A_q with a histogram of reference atypicality scores for said selected phase for a collection of at least one reference flight.

6. The method of claim 4, further comprising:

when said atypicality score A_q is greater than a selected percentage, PCT, of all atypicality scores in said histogram, interpreting this condition as indicating that a selected phase (ph) for said selected flight is atypical, as compared to a percentage of said reference atypicality scores, where PCT is a selected number at least equal to 80 percent.

7. The method of claim 6, further comprising choosing said selected percentage PCT from a group of percentages consisting of 80 percent, 90 percent, 95 percent and 99 percent.

8. The method of claim 6, further comprising selecting said phase of said selected flight from among the phases pre-takeoff taxi, pre-takeoff position, takeoff, low altitude ascent, high altitude ascent, cruise, high altitude descent, low altitude descent, runway approach, touchdown and post-touchdown taxi.

9. The method of claim 4, further comprising computing a p-value associated with said atypicality score A_q, defined as

p(q;

ph)=F1·

F2/(F3·

F4·

F5),

F1=|A_q|<sup>(R−

M−

1)</sup>

F2=exp(−

(½

) trace(Σ

<sup>−

1</sup> A_q))

F3=2−

^MR*π

<sup>M(M−

1)/4</sup>

F4=|Σ

|^1/2R

F5=Π

^M_i=1Γ

{(1/2)(R+1−

i)},

where r(x) is an incomplete gamma function.

10. The method of claim 9, further comprising:

assigning each of a group of observation vectors U, whose entries are drawn from entries of said transformed matrix G, to one of two or more clusters, using a selected cluster analysis procedure;

for each modified cluster, providing a cluster membership score CMS(q;

ph) that is a strictly monotonic function of the number of observation vectors U in the cluster divided by the total number of observation vectors in all clusters; and

computing a global atypicality score, GAS, defined as

GAS(q;

ph)=w*Fn{p(q;

ph)}+(1−

w)*Fn{CMS(q;

ph)}, where Fn is a selected monotonic function and w is a selected weight lying between 0 and 1.

11. The method of claim 10, further comprising selecting said monotonic function Fn to be Fn{s}=−

log_z{s}, where z is a selected number greater than 1.

12. The method of claim 10, wherein said selected cluster analysis procedure comprises:

(1) providing an initial set of at least two clusters

(2) providing a cluster centroid for each cluster;

(3) assigning each of said group of observation vectors U, whose entries are drawn from entries of said transformed matrix G, to the cluster for which a distance from the centroid to said vector U is a minimum among all centroids;

(4) computing a modified centroid for each cluster from said vectors U assigned to the cluster;

(5) assigning each of said vectors U to a modified cluster associated with the modified centroid for which the distance from the modified centroid to said vector U is a minimum among the distance for all modified centroids;

(6) repeating steps 3, 4 and 5 until at least one of two conditions is met;

(i) the number of iterations is greater that a maximum allowed number of iterations, or (ii) the number of flights that change cluster membership between iterations is below a selected threshold; and

(7) for each modified cluster, providing said cluster membership score CMS(q;

ph).

13. The method of claim 10, further comprising:

comparing said computed GAS for said computed atypicality score A_q with GAS scores for at least first, second and third atypicality scores A_q; and

estimating a level of atypicality for the first computed atypicality, based upon number of GAS that are less than the first computed GAS and number of GAS that are greater than the first computed GAS.

14. The method of claim 10, further comprising:

when said computed GAS for said computed atypicality score A_q lies in a selected atypicality range, interpreting this condition as indicating that said flight parameter values for at least one phase ph for said flight number q are atypical.

15. The method of claim 10, further comprising:

when said computed GAS for said computed atypicality score A_q does not lie in a selected atypicality range, interpreting this condition as indicating that at least one of said flight parameter values for at least one phase ph for said flight number q is not atypical.

16. The method of claim 10, wherein said selected cluster analysis procedure comprises a hierarchical cluster analysis procedure.

17. The method of claim 1, further comprising:

including in said vector E1 at least one of;

(i) a sequence of beginning values, denoted begin(v), for each of said vectors v=A, v=B, v=C and v=D, and (ii) a sequence of ending values, denoted end(v), for each of said vectors v=A, v=B, v=C and v=D.