Identifying failures in an aeroengine

1. A method of identifying failures in an aeroengine, the method comprising:

using sensors to collect time-series measurements from said aeroengine and its environment;

using a processor means to calculate from said time-series measurements indicators that are specific to elements of said aeroengine;

using the processor means to define from said specific indicators a set of standardized indicators that are representative of an operation of said aeroengine;

using the processor means to construct an anomaly vector representative of a behavior of said engine as a function of said set of standardized indicators;

using the processor means, in an event of an abnormality being revealed by said anomaly vector, to select a subset of reference vectors having directions belonging to a determined neighborhood of a direction of said anomaly vector, said subset of reference vectors being selected from a set of reference vectors associated with failures of said aeroengine and determined using criteria established by experts; and

using the processor means to identify the failures associated with said subset of reference vectors;

and wherein the selecting said subset of reference vectors comprises:

using the processor means to calculate geodesic distances between a projection of said anomaly vector and projections of said reference vectors on a sphere in a space of dimension equal to a number of indicators in said set of standardized indicators minus a number of linear relationships between the indicators;

using the processor means to compare said geodesic distances in pairs;

using the processor means to classify the reference vectors in increasing order of their geodesic distances relative to said anomaly vector; and

using the processor means to form said subset of reference vectors from first reference vectors having a classification order less than a determined rank.

2. A method according to claim 1 , wherein said sphere is of radius 1 .

3. A method according to claim 1 , further comprising:

using the processor means to define, for each reference vector, an a priori probability of occurrence on the basis of criteria established by experts; and

using the processor means to calculate, for each reference vector, an a posteriori probability of occurrence as a function of said a priori probability of occurrence and of said geodesic distances.

4. A method according to claim 1 , wherein said set of standardized indicators {tilde over ({tilde over (y)} 1 , . . . {tilde over ({tilde over (y)} m comprises indicators {tilde over (y)} 1 , . . . {tilde over (y)} n identified by the processor means using criteria established by experts.

5. A method according to claim 4 , wherein said set of standardized indicators {tilde over ({tilde over (y)} 1 , . . . {tilde over ({tilde over (y)} m further comprises dynamic indicators constructed by the processor means as a function of the indicators at present and past instants {tilde over ({tilde over (y)}(t)=f({tilde over (y)}(s);s≦t) and representative of the behavior of said aeroengine over time.

6. A method according to claim 1 , wherein said constructing said anomaly vector comprises:

using the processor means to form an indicator vector {tilde over ({tilde over (y)} from said set of indicators; and

using the processor means to construct said anomaly vector z by renormalizing said indicator vector {tilde over ({tilde over (y)} using formula:

z=Σ −1/2 ({tilde over ({tilde over ( y )}−μ)

in which μ is the mean of the indicator vectors and Σ −1/2 is the root of a pseudo-inverse signal Σ −1 of a covariance matrix Σ.

7. A method according to claim 6 , further comprising:

using the processor means to calculate the norm of said anomaly vector using a Mahalanobis distance:

d 2 =∥z∥ 2 =({tilde over ({tilde over ( y )}−μ) T Σ −1 ({tilde over ({tilde over ( y )}−μ); and

using the processor means to detect an abnormality of said aeroengine using a trigger threshold defined as a function of statistical distribution of said norm of the anomaly vector.

8. A method according to claim 1 , wherein said set of reference vectors is constructed in accordance with caricatural behaviors of the indicators in the event of anomalies.

9. A method according to claim 3 , further comprising:

using the processor means to establish a decision grid in application of criteria established by experts;

using the processor means to apply Bayesian rules to deduce per component probabilities of failures from said a posteriori probabilities of occurrence and from said decision grid; and

using the processor means to detect faulty physical components that are responsible for said failures in application of said per component failure probabilities.

10. A method according to claim 9 , wherein said decision grid is formed of a matrix of conditional probabilities that a component is faulty, knowing that a failure has been observed and of a series of coefficients corresponding to a priori probabilities of failure of each component.

11. A method according to claim 9 , wherein said decision grid is corroborated by machine learning.

12. A non-transitory computer readable medium including computer executable instructions for implementing the method of identifying failures according to claim 1 , when executed by a processor.

13. A system for identifying failures in an aeroengine, the system comprising:

sensors for collecting time-series measurements from said aeroengine and its environment;

means for calculating from said time-series measurements indicators that are specific to elements of said aeroengine;

means for using said specific indicators to define a set of standardized indicators representative of an operation of said aeroengine;

means for constructing an anomaly vector representative of a behavior of said engine as a function of said set of standardized indicators;

means for selecting in an event of an anomaly being revealed by said anomaly vector a subset of reference vectors having directions belonging to a determined neighborhood of a direction of said anomaly vector, said subset of reference vectors being selected from a set of reference vectors associated with failures of said aeroengine and determined using criteria established by experts;

means for identifying the failures associated with said subset of reference vectors;

and wherein the means for selecting a subset of reference vectors comprises:

means for calculating geodesic distances between a projection of said anomaly vector and projections of said reference vectors on a sphere in a space of dimension equal to a number of indicators of said set of standardized indicators minus a number of linear relationships between said indicators;

means for comparing said geodesic distances in pairs;

means for classifying the reference vectors in an increasing order of their geodesic distances relative to said anomaly vector; and

means for forming said subset of reference vectors from first reference vectors having a classification order less than a determined rank.

14. A system according to claim 13 , further comprising:

means for defining, for each reference vector, an a priori probability of occurrence in application of criteria established by experts; and

means for calculating, for each reference vector, an a posteriori probability of occurrence as a function of said a priori probability of occurrence and of said geodesic distances.

15. A system according to claim 13 , further comprising:

means for establishing a decision grid in application of criteria established by experts;

means for using Bayesian rules to deduce per component failure probabilities from said a posteriori probabilities of occurrence and from said decision grid; and

means for detecting faulty physical components that are responsible for said failures according to said per component failure probabilities.