In this paper we present the application of convolutional neural networks to identify faults in selected aircraft components. The theoretical part of the article includes the definition and discussion of machine learning issues. In the simulation part, the input data of the network in the form of a set of images of selected aircraft components were prepared and the architecture of the convolutional neural network was created. Experimental results confirming the high efficiency of aircraft damage identification using the proposed convolutional neural network architecture.
Aircraft Diagnostics Using Convolutional Neural Networks
03.06.2024
786221 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch