Abstract: A method is provided for characterizing a part. The method includes a) carrying out non-destructive measurements using a sensor, the sensor being placed on the part or facing the part; b) using the measurements as input data of a neural network; and c) depending on the value of each node of the output layer of the neural network, characterizing the part. The method further includes prior to steps b) and c): constructing a first database, on a first model part; employing a first neural network parametrized by a first training operation, using the first database; constructing a second database, containing experimental measurements of the physical quantity performed on the part to be characterized; a second training operation, using the second database, so as to parametrize a second neural network, using the parametrization of the first neural network. In step c), the neural network used is the second neural network.

Abstract: Method and a device for detecting defects in an elongated structure able to act as an elastic-wave waveguide, the elongated structure being fitted with a plurality of transducers able to acquire measurement signals of characteristic quantities of elastic waves propagating in the elongated structure, each transducer being coupled to an electronic device, the set of electronic devices constituting a network of nodes configured to process measurement signals received from at least one transducer.

Abstract: A method is provided for characterizing a part includes: a) carrying out measurements using a sensor, the sensor being placed on the part or facing the part; b) forming at least one measurement matrix using the measurements performed in step a); c) using the matrix as input datum of a convolutional neural network including an extracting block, configured to extract features from each input datum; a classifying block, configured to classify the features extracted, the classifying block outputting to at least one node; and d) depending on each node, detecting the presence of a defect in the part. The neural network employed in step c) is established using the extracting block of another previously parametrized neural network.