A method and a system for standardizing data used for monitoring an aeroengine, and including: operating over time to collect time-series measurements from the aeroengine; calculating from the time-series measurements a set of indicators Y=(y1, . . . , yj, . . . , ym) specific to elements of the eng
A method and a system for standardizing data used for monitoring an aeroengine, and including: operating over time to collect time-series measurements from the aeroengine; calculating from the time-series measurements a set of indicators Y=(y1, . . . , yj, . . . , ym) specific to elements of the engine; identifying from the time-series measurements an exogenous data set X=(x1, . . . , xn) representative of external context acting on the set of indicators Y; defining a conditional multidimensional model simultaneously handling the indicators of the set of indicators Y while taking account of the exogenous data set X to form a set of estimators Ŷ=(ŷ1, . . . , ŷj, . . . , ŷm) corresponding to the set of indicators Y=(y1, . . . , yj, . . . , ym); and normalizing each estimator ŷj as a function of a reference value for the corresponding indicator yj and of a difference between each estimator ŷj and corresponding indicator yj so as to form a set of standardized values {tilde over (Y)}=({tilde over (y)}1, . . . , {tilde over (y)}j, . . . , {tilde over (y)}m).
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1. A method of standardizing data used for monitoring an aeroengine, the method comprising: collecting time-series measurements over time concerning said aeroengine;from said time-series measurements, calculating a set of indicators Y=(y1, . . . , yj, . . . , ym) that are specific to elements of sai
1. A method of standardizing data used for monitoring an aeroengine, the method comprising: collecting time-series measurements over time concerning said aeroengine;from said time-series measurements, calculating a set of indicators Y=(y1, . . . , yj, . . . , ym) that are specific to elements of said engine;from said time-series measurements, identifying an exogenous data set X=(x1, . . . , xn), representative of external context acting on said set of indicators Y;for each indicator yj of said set of indicators Y, constructing a projection space E(j)=σ(Y(j), X) generated by analytic transformations of a subset of indicators Y(j)=(y1, . . . yj−1, yj+1, . . . ym) comprising all of the indicators of said set of indicators Y except each said indicator yj and by said exogenous data set X;for each indicator yj of said set of indicators Y, calculating a corresponding estimator ŷj by projecting said indicator yj using a regression technique onto said projection space E(j)=σ(Y(j), X), thereby forming a set of estimators Ŷ=(ŷ1, . . . , ŷj, . . . , ŷm) corresponding to said set of indicators Y=(y1, . . . , yj, . . . , ym); andnormalizing each estimator ŷj as a function of a reference value for the corresponding indicator yj and of a difference between each said estimator ŷj and said corresponding indicator yj to form a set of standardized values {tilde over (Y)}=({tilde over (y)}1, . . . {tilde over (y)}j, . . . , {tilde over (y)}m). 2. A method according to claim 1, wherein said time-series measurements are collected during normal operation of said aeroengine. 3. A method according to claim 1, wherein each standardized value {tilde over (y)}j is calculated by adding a mean or reference value for the corresponding indicator yj to a difference between the corresponding indicator yj and the corresponding estimator ŷj, using the following equation: {tilde over (y)}j= yj+(yj−ŷj). 4. A method according to claim 1, further comprising analyzing robustness of each estimator using a cross evaluation technique serving to select an optimum projection space. 5. A method according to claim 1, wherein said projection space is constructed using expert criteria with help of physical formulations of relationships between the indicators and between the indicators and the exogenous data. 6. A method according to claim 1, wherein said space is constructed automatically by using a neural network. 7. A method according to claim 6, wherein said neural network is a model having nodes. 8. A method according to claim 1, wherein the indicators of said set of indicators Y=(y1, . . . , yj, . . . , ym) are specific to physical and/or logical elements of said engine. 9. A method according to claim 1, wherein the indicators of said set of indicators Y=(y1, . . . , yj, . . . , ym) are calculated using expert criteria by constructing an FMECA. 10. A method according to claim 1, wherein the indicators are identified by referencing particular points or particular functions summarizing details or shapes of certain curves representative of said time-series measurements. 11. A method according to claim 1, wherein the exogenous data X=(x1, . . . , xn) acting on the indicators is identified using expert criteria by dependency analysis enabling context data associated with the indicators to be listed. 12. A method according to claim 1, wherein said regression is a linear regression. 13. A system for standardizing data used for monitoring an aeroengine, the system comprising: means for operating over time to collect time-series measurements from said aeroengine;means for calculating from said time-series measurements a set of indicators Y=(y1, . . . yj, . . . , ym) specific to elements of said engine;means for identifying from said time-series measurements an exogenous data set X=(x1, . . . , xn) representative of external context acting on said set of indicators Y;means for constructing for each indicator yj of said set of indicators Y, a projection space E(j)=σ(Y(j), X) generated by analytic transformations of a subset of indicators Y(j)=(y1, . . . , yj−1, yj+1, . . . , ym) comprising all of the indicators of said set of indicators Y except each said indicator yj, and by said exogenous data set X;means for calculating for each said indicator yj of said set of indicators Y a corresponding estimator ŷj by using a regression technique to project each said indicator yj onto said projection space E(j)=σ(Y(j), X), forming a set of estimators Ŷ=(ŷ1, . . . , ŷj, . . . , ŷm) corresponding to said set of indicators Y=(y1, . . . , yj, . . . , ym); andmeans for normalizing each estimator ŷj as a function of a reference value for the corresponding indicator yj and of a difference between each said estimator ŷj and said corresponding indicator yj so as to form a set of standardized values {tilde over (Y)}=({tilde over (y)}1, . . . , {tilde over (y)}j, . . . , {tilde over (y)}m). 14. A non-transitory computer readable medium including computer executable instructions for implementing the standardization method according to claim 1 when executed by a processor.
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Ko Gary Kam-Yuen,CAXITX M4C 5P6, Method and apparatus for non-invasive diagnosis of cardiovascular and related disorders.
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