A method for intelligent quality assessment of a gearbox using antipatterns and convolutional neural networks

Abstract

Taking gearbox as a reference structure, authors apply a method for grading the quality of mechanical structures using a convolutional neural network trained with antipatterns found in gearbox constructions. Antipatterns are used as a quality reference embodied in a neural network, which is used for classifying tested structures to match the antipatterns taught to it. The measure of similarity to antipatterns (used for training and abstracted by the neural network) is interpreted as the quality measure and so the inversed sum of similarities to each of the antipattern classes used in training is considered a quantitative grade of quality. Such grading enables automated cross-comparison of structures based on their quality (defined as differentiation from used antipatterns).