Abstract: A method for the acoustic analysis of a machine (M) including the acquisition of at least one acoustic signal supplied by at least one microphone (7) positioned in the machine, characterized in that it comprises the following steps: separation of at least one acoustic signal into a plurality of sound sources, the signal being modelled as a mixture of components, each one corresponding to a sound source; for at least one separated sound source, determination of a characteristic acoustic signature; comparison of at least one characteristic acoustic signature with at least one reference acoustic signature recorded in a reference database (5).

Abstract: A method and a system for the vibration analysis of an engine, including acquiring vibration signals relating to the engine and in order to form at least one spectrogram that represents an operating state of the engine, selecting at least one zone of interest in at least one spectrogram, comparing each zone of interest with a set of corresponding comparison zones belonging to annotated spectrograms recorded in a reference database, and determining a subset of comparison zones that have vibration behaviors similar to those of the zone of interest, and displaying the subset of comparison zones and annotations associated with the subset of comparison zones.

Abstract: A system for monitoring a set of components of a device, including: a detecting system including a set of agents for detecting anomalies, with each agent receiving measurements on physical parameters relating to a sub-set of components of a device and delivering an initial distribution of the probability of an anomaly; an acquiring system which receives feedback information and the initial distributions of the probability of an anomaly from the agents; an emerging system which iteratively emerges current distributions of the probability of an anomaly, with the current distributions of the probability of an anomaly converging towards a set of optimum distributions of the probability of an anomaly; and a synthesizing system which synthesizes optimum distributions of the probability of an anomaly relating to at least one sub-set of interest of components of the device in order to extract the risks of an anomaly specific to the sub-set of interest.

Abstract: A method for the acoustic analysis of a machine (M) including the acquisition of at least one acoustic signal supplied by at least one microphone (7) positioned in the machine, characterized in that it comprises the following steps: separation of at least one acoustic signal into a plurality of sound sources, the signal being modelled as a mixture of components, each one corresponding to a sound source; for at least one separated sound source, determination of a characteristic acoustic signature; comparison of at least one characteristic acoustic signature with at least one reference acoustic signature recorded in a reference database (5).

Abstract: A method and a system for the vibration analysis of an engine, including acquiring vibration signals relating to the engine and in order to form at least one spectrogram that represents an operating state of the engine, selecting at least one zone of interest in at least one spectrogram, comparing each zone of interest with a set of corresponding comparison zones belonging to annotated spectrograms recorded in a reference database, and determining a subset of comparison zones that have vibration behaviours similar to those of the zone of interest, and displaying the subset of comparison zones and annotations associated with the subset of comparison zones.

Abstract: The invention relates to a system for monitoring a set of components of a device such as an engine, comprising: means for detecting (2) comprising a set of agents for detecting anomalies (a1, . . . , ai, . . .

Abstract: A method and system for detecting anomalies in an aeroengine. The method and system define a behavior model of a controller of the aeroengine using a time regression modeling the behavior of the controller as a function of a data set relating to the controller and including measurements of past behaviors and command and state measurements of the controller; continuously recalculate the behavior model for each new data set; and monitor statistical variation of the behavior model to detect a behavior anomaly of the controller representing an operating anomaly of the engine.

Abstract: A method and a system for identifying failures in an aeroengine. The system includes: a mechanism defining a set of standardized indicators representative of operation of the aeroengine; a mechanism constructing an anomaly vector representative of a behavior of the engine as a function of the set of standardized indicators; a mechanism selecting in an event of an anomaly being revealed by the anomaly vector a subset of reference vectors having directions belonging to a determined neighborhood of a direction of the anomaly vector, the subset of reference vectors being selected from a set of reference vectors associated with failures of the aeroengine and determined using criteria established by experts; and a mechanism identifying failures associated with the subset of reference vectors.

Abstract: 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 ?=(?1, . . . , ?j, . . . , ?m) corresponding to the set of indicators Y=(y1, . . . , yj, . . . , ym); and normalizing each estimator ?j as a function of a reference value for the corresponding indicator yj and of a difference between each estimator ?j and corresponding indicator yj so as to form a set of standardized values {tilde over (Y)}=({tilde over (y)}1, . . .

Abstract: 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 ?=(?1, . . . , ?j, . . . , ?m) corresponding to the set of indicators Y=(y1, . . . , yj, . . . , ym); and normalizing each estimator ?j as a function of a reference value for the corresponding indicator yj and of a difference between each estimator ?j and corresponding indicator yj so as to form a set of standardized values {tilde over (Y)}=({tilde over (y)}1, . . .

Abstract: A method and a system for identifying failures in an aeroengine. The system includes: a mechanism defining a set of standardized indicators representative of operation of the aeroengine; a mechanism constructing an anomaly vector representative of a behavior of the engine as a function of the set of standardized indicators; a mechanism selecting in an event of an anomaly being revealed by the anomaly vector a subset of reference vectors having directions belonging to a determined neighborhood of a direction of the anomaly vector, the subset of reference vectors being selected from a set of reference vectors associated with failures of the aeroengine and determined using criteria established by experts; and a mechanism identifying failures associated with the subset of reference vectors.

Abstract: A method and system for detecting anomalies in an aeroengine. The method and system define a behavior model of a controller of the aeroengine using a time regression modeling the behavior of the controller as a function of a data set relating to the controller and including measurements of past behaviors and command and state measurements of the controller; continuously recalculate the behavior model for each new data set; and monitor statistical variation of the behavior model to detect a behavior anomaly of the controller representing an operating anomaly of the engine.