Title :
Using artificial neural networks to improve the mechanical signature analysis test
Author :
DeBrunner, Victor ; Bussert, Tod
Author_Institution :
Dept. of Electr. Eng., Oklahoma Univ., Norman, OK, USA
Abstract :
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
Keywords :
ART neural nets; computer equipment testing; computer testing; dynamic testing; electrical engineering computing; hard discs; machine testing; motor drives; pattern classification; pattern recognition; self-organising feature maps; small electric machines; spectral analysis; artificial neural networks; input reduction network; mechanical signature analysis; motor classification; motor power spectral density; self-organizing adaptive resonance structure; spindle motors; Artificial neural networks; Cities and towns; Costs; Data mining; Frequency estimation; Process control; Resonance; Shape; Switching frequency; Testing;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-1775-0
DOI :
10.1109/ICASSP.1994.389579
