Using artificial neural networks to improve the mechanical signature analysis test
Victor DeBrunner, T. Bussert · 2002
A faster, more cost effective test for evaluating spindle motors is described. This test is significant in proving the efficacy of the potentials of artificial neural networks in industrial situations. The use of a self-organizing adaptive resonance structure following an input reduction network is studied. This network extracts the information about the motor power spectral density which is vital to the motor classification. Some heuristic rules are developed to help guide the test designer. Classification shapes are examined to determine the influence of the neural network on the motor classification.>