Abstract: A flight data evaluation system to optimise operations of an aircraft, comprising: an acquisition circuit (3) configured to collect observation data (D1?Dn) related to a fleet of aircraft, and a processing circuit (5) configured: to assign quality values (Qi) to the observation data by applying predetermined learning models (M1?Mn) to them corresponding to their contexts, thus generating observation data enriched with quality values, to define indicators (21, 23) relative to specific elements of the aircraft, to compare the quality value (Qi) of each observation data (Di) with a predetermined threshold (S), and to analyse the observation data as a function of their quality values so as to optimise aircraft operations.

Abstract: A method of estimating future change in operation of a monitored aircraft (A), including the following steps performed by a computer on board the monitored aircraft, to calculate a current state of the monitored aircraft (ECA) from measurements (VFA) of variables related to operation of the monitored aircraft, to send a request to analyse the similarity of the calculated current state with previous states (EPB) of similar aircraft, and to analyse change in operation (SPB) of each similar aircraft having a similar previous state to determine a probable change (FPA) in operation of the monitored aircraft. The method includes a step performed by a computer (B) on board each aircraft, to compare the calculated current state (ECA) with previous states (EPB) of similar aircraft, and send the change in operation (SPB) corresponding to an identified similar previous state. The invention includes an on board system capable of implementing the method.

Abstract: A method and a system for monitoring an aircraft engine (2), including: acquisition and processing part (11) configured to collect a time signal of the exhaust gas temperature residual margin of the aircraft engine (2), acquisition and processing part (11) configured to smooth the time signal thus forming a first curve representing the temperature residual margin, acquisition and processing part (11) configured to identify decreasing pieces in the first curve, acquisition and processing part (11) configured to construct a second curve by concatenation of the decreasing pieces, the second curve being continuous while being restricted to the decreasing pieces of the first curve, acquisition and processing part (11) configured to construct a prediction model from the second curve to determine at least one failure forecast indicator.

Abstract: A method of estimation on a curve of a relevant point for detecting an anomaly of a motor. The method includes selecting a profile including a binary code, each component of which codes a direction of variation between two consecutive characteristic points of at least one learning curve, a model making it possible to estimate a relevant point based on a set of characteristic points of a curve and a filter. The method also includes applying the filter of the profile selected to the curve, determining a set of characteristic points of the filtered curve and of a binary code, comparing the determined code and the code of the profile selected, and estimating, as a function of the comparison, the relevant point on the curve based on the characteristic points of the filtered curve and the model of the profile selected.

Abstract: A method monitoring an aircraft engine, including: collecting time history measurements on a first set of measurements on endogenic variables and a second set of measurements on exogenic variables; standardizing measurements related to the endogenic variables considering the exogenic variables to form an entry point corresponding to a condition of a current engine in a multi-dimensional space, for each acquisition; projecting each multi-dimensional space entry point onto an image point on a previously constructed map, forming a sequence of image points on the map reflecting a distribution of the multi-dimensional space entry points, the image points forming a current trajectory representing a change to conditions of the current engine; comparing an end of the current trajectory with previously recorded portions of aircraft engine trajectories kept in a database; and analyzing statistical variations of the trajectories to anticipate maintenance operations on the current engine.

Abstract: The invention relates to a system of non-regression tests of a designing tool including modules (13) used to build a monitoring device (11) of an aircraft engine (7), said system of tests including: processing means (9) configured to automatically create an experience base (9e) by instrumenting behaviour tests upon executing the modules, said experience base including reference input signals (SIN—ref), sets of reference parameters, reference output signals (SOUT—ref), and collections of results of reference executions relating to said modules, processing means (9) configured to start again said behaviour tests on said modules (13) depending on the corresponding reference input signals and the corresponding sets of reference parameters thus generating test output signals (SOUT—test) and collections of results of test executions, and processing means (9) configured to compare said test output signals with the corresponding reference output signals and said collections of results of test execution with said c

Abstract: A device for detecting anomalies in an aircraft turbine engine by acoustic analysis, the device not an onboard device and including: a mobile module including a directional system for acquiring acoustic signals from the turbine engine; a processor for processing the signals, which is suitable for generating a damage report; a transmitter for transmitting the damage report; a server capable of exchanging data with the mobile module, the server including a receiver for receiving the damage report; and a storage device suitable for storing the damage report.

Abstract: A method monitoring an aircraft engine, including: collecting time history measurements on a first set of measurements on endogenic variables and a second set of measurements on exogenic variables; standardizing measurements related to the endogenic variables considering the exogenic variables to form an entry point corresponding to a condition of a current engine in a multi-dimensional space, for each acquisition; projecting each multi-dimensional space entry point onto an image point on a previously constructed map, forming a sequence of image points on the map reflecting a distribution of the multi-dimensional space entry points, the image points forming a current trajectory representing a change to conditions of the current engine; comparing an end of the current trajectory with previously recorded portions of aircraft engine trajectories kept in a database; and analyzing statistical variations of the trajectories to anticipate maintenance operations on the current engine.

Abstract: A monitoring method and system for a test bench for at least one engine component, including: an acquisition mechanism to acquire time signal packets corresponding to measurements of endogenic and exogenic parameters specific to the combination of the test bench and the engine component, at successive instants; and a processor to construct an endogenic indicator vector and an associated exogenic indicator factor at each instant of the successive instants, using time signal packets earlier than the instant, to identify a context class for the exogenic indicator vector, and to calculate a risk probability of the endogenic indicator vector conditioned by the identified context class for the associated exogenic indicator vector using at least one anomaly detector, to produce a diagnostic of a state of the test bench and engine component combination.

Abstract: A global predictive monitoring system for a manufacturing facility. The system may be employed in an integrated circuit (IC) device fabrication facility to monitor processing of semiconductor wafers. The system may include deployment of a swarm of individually separate agents running in computers in the facility. Each agent may comprise a genetic algorithm and use several neural networks for computation. Each agent may be configured to receive a limited set of inputs, such as defectivity data and WIP information, and calculate a risk from the inputs. A risk may be a value indicative of a production yield. Each agent may also generate a quality value indicative of a reliability of the risk value. New agents may be generated from the initial population of agents. Outputs from the agents may be collected and used to calculate projections indicative of a trend of the production yield.

Abstract: A global predictive monitoring system for a manufacturing facility. The system may be employed in an integrated circuit (IC) device fabrication facility to monitor processing of semiconductor wafers. The system may include deployment of a swarm of individually separate agents running in computers in the facility. Each agent may comprise a genetic algorithm and use several neural networks for computation. Each agent may be configured to receive a limited set of inputs, such as defectivity data and WIP information, and calculate a risk from the inputs. A risk may be a value indicative of a production yield. Each agent may also generate a quality value indicative of a reliability of the risk value. New agents may be generated from the initial population of agents. Outputs from the agents may be collected and used to calculate projections indicative of a trend of the production yield.

Abstract: A global predictive monitoring system for a manufacturing facility. The system may be employed in an integrated circuit (IC) device fabrication facility to monitor processing of semiconductor wafers. The system may include deployment of a swarm of individually separate agents running in computers in the facility. Each agent may comprise a genetic algorithm and use several neural networks for computation. Each agent may be configured to receive a limited set of inputs, such as defectivity data and WIP information, and calculate a risk from the inputs. A risk may be a value indicative of a production yield. Each agent may also generate a quality value indicative of a reliability of the risk value. New agents may be generated from the initial population of agents. Outputs from the agents may be collected and used to calculate projections indicative of a trend of the production yield.