Abstract: A method for detecting an anomaly in a device of interest belonging to a fleet of devices, includes: acquiring a new dataset, with each data item representing the value of a respective quantity of the device of interest; updating a main database, for adding the new dataset thereto; weighting the datasets of the main database; extracting, from the main database, a reference database consolidating datasets associated with devices of the fleet of devices, excluding datasets associated with the device of interest; extracting, from the main database, an analysis database consolidating datasets associated with the device of interest; comparing, for at least one quantity, corresponding data of the analysis and reference databases; and detecting an anomaly in the device of interest, when, for at least one of the quantities, the corresponding data of the analysis database deviates beyond a predetermined threshold relative to the corresponding data of the reference database.

Abstract: A system and method for determining a value of a flow rate of a liquid in a vehicle engine system comprising a fluid tank (3), a pump (2), a fluid injector (1), with a fluid flow path from the pump to an injected zone (4), and an electronic control unit (5) for controlling opening of the injector, the method comprising:—providing a loss estimation module (52), supplying as output a hydraulic loss coefficient (CP),—carrying out a plurality of sequences of fluid injection, with values of a plurality of parameters (dP, P1, P0, T, X) being collected,—calculating a theoretical quantity (QTH) of fluid injected during these injection sequences, with the aid of the values of the parameters (P1, P0, T, X),—calculating an estimated actual quantity (QRE) of fluid injected during the injection sequences, by applying the loss coefficient (CP) to the calculation of the theoretical quantity of fluid.

Abstract: A method and system for determining iteratively a quantity of interest used to control at least one component of a motor-vehicle powertrain. The system includes a supervised-learning-based estimating module, for example a neural network, which receives as input a set of first input data, and delivers as output at least one intermediate output quantity, a likelihood-checking module, which receives as input the set of first input data and a set of second input data, and which delivers as output a likelihood index, a downstream processing module which receives as input the likelihood index and the intermediate output quantity, and which delivers as output a final output quantity. When the likelihood index is good, the final output quantity is obtained from the intermediate output quantity, otherwise the final output quantity is obtained from a model of a physical representation.

Abstract: A method and system for determining iteratively a quantity of interest used to control at least one component of a motor-vehicle powertrain. The system includes a supervised-learning-based estimating module, for example a neural network, which receives as input a set of first input data, and delivers as output at least one intermediate output quantity, a likelihood-checking module, which receives as input the set of first input data and a set of second input data, and which delivers as output a likelihood index, a downstream processing module which receives as input the likelihood index and the intermediate output quantity, and which delivers as output a final output quantity. When the likelihood index is good, the final output quantity is obtained from the intermediate output quantity, otherwise the final output quantity is obtained from a model of a physical representation.