Abstract: A method for determining an efficiency fault of at least one module of a turboshaft engine of an aircraft. The method comprising a step of determining an estimated real mapping, a step of determining real indicators from the estimated real mapping, a step of determining a plurality of simulated mappings from a simulation of a theoretical model of the turboshaft engine for different efficiency configurations, a step of determining simulated indicators for each simulated mapping, a step of training a mathematical model by coupling the simulated indicators with efficiency configurations, and a step of applying said mathematical model to the real indicators so as to deduce therefrom a real efficiency configuration.

Abstract: A device for monitoring a state of a propulsion engine includes an acquisition module that acquires data of flights of the propulsion engine, comprising, for each flight, values of input variables, environment variables, and output variables of the propulsion engine during the flight, a learning module that computes, by learning from the data of each flight, an individual flight model for the flight, a using module that computes, for each flight, estimates of the values of the output variables, by applying the individual flight model to reference values of the input variables and the environment variables, and an error associated with the estimates of the values of the output variables that is obtained by applying the individual flight model to the reference values of the input variables and the environment variables. The reference values belong to a set of reference data, which are identical for the individual flight models.

Abstract: A method for evaluating the state of an aircraft engine for a given flight, each flight of the aircraft being associated with one time-domain series, includes, each time maintenance of the aircraft is performed, creating a dataset including the time-domain series associated with each flight carried out between the maintenance and the preceding maintenance; creating a set of datasets including each created dataset and dividing it into a training set and a validating set; conjointly training an embedding function and a classifier, selecting a reference time-domain series from a time-domain series of the validating set; and computing a distance between the embedment associated with the time-domain series of the given flight and the embedment of the reference time-domain series, with a view to computing an indicator of engine state.

Abstract: A device for monitoring a state of a propulsion engine includes an acquisition module that acquires data of flights of the propulsion engine, comprising, for each flight, values of input variables, environment variables, and output variables of the propulsion engine during the flight, a learning module that computes, by learning from the data of each flight, an individual flight model for the flight, a using module that computes, for each flight, estimates of the values of the output variables, by applying the individual flight model to reference values of the input variables and the environment variables, and an error associated with the estimates of the values of the output variables that is obtained by applying the individual flight model to the reference values of the input variables and the environment variables. The reference values belong to a set of reference data, which are identical for the individual flight models.

Abstract: A system for monitoring an aircraft engine, including an acquisition module for acquiring current measurements of physical quantities, referred to as input and output physical sizes, relative to the aircraft engine, a module for simulating the physical behavior of the aircraft engine, in order to simulate output physical quantities as a function of the current measurements of input physical sizes, a processor for calculating physical margins, referred to as actual physical margins, between the simulated values of output physical quantities and the corresponding current measurements of output physical quantities, a learning module for predicting margins, the margins being predicted using current measurements of input physical quantities and wherein the processor is configured to calculate surveillance residuals giving an indication of the state of the aircraft engine.

Abstract: A system monitors the health of a helicopter, and includes a device for determining a change of state of the engine and is configured to collect data measured by engine and external conditions sensors during a stable flight phase and to process the measured data.

Abstract: The invention relates to a method for monitoring the soundness of helicopters comprising the determination of the severity of a plurality of flight missions of a plurality of helicopters, comprising a step for acquiring and storing flight data from helicopter flight missions, and a step for acquiring and storing maintenance data from the plurality of helicopters. The method is characterised in that said determination comprises a mission-type construction step, comprising a sub-step for constructing descriptors, a sub-step for partitioning the descriptors and a sub-step for allocating a mission type to each flight by associating the descriptor of said flight and a sub-set, in which this descriptor is found, and a step for interpreting the severity of the mission types, comprising a sub-step for estimating the severity models, and a sub-step for associating a severity model with each mission type determined in the mission type construction step.

Abstract: The invention relates to a method for monitoring the soundness of helicopters comprising the determination of the severity of a plurality of flight missions of a plurality of helicopters, comprising a step for acquiring and storing flight data from helicopter flight missions, and a step for acquiring and storing maintenance data from the plurality of helicopters. The method is characterised in that said determination comprises a mission-type construction step, comprising a sub-step for constructing descriptors, a sub-step for partitioning the descriptors and a sub-step for allocating a mission type to each flight by associating the descriptor of said flight and a sub-set, in which this descriptor is found, and a step for interpreting the severity of the mission types, comprising a sub-step for estimating the severity models, and a sub-step for associating a severity model with each mission type determined in the mission type construction step.