Title :
Application of neural networks to acoustic screening of small electric motors
Author :
Murphy, Seibert L. ; Sayegh, Samir I.
Author_Institution :
Automation Engineering, Inc., Ft. Wayne, IN, USA
Abstract :
A three-layer backpropagation neural network trained to differentiate good versus bad electric motors based on aural cues is described. Training performance of 100% and test performance of greater than 95% is achieved. Motors are classified as `good´ (pass) and `bad´ (fail) by a human operator. Acoustic data constitute a continuous signal in the form of a sound pressure level processed into nine bands from 1 kHz through 10 kHz. The Galatea neural network simulator is used to model two common neural network paradigms (linear and backpropagation) for suitability in this problem. Preprocessing of data is necessary. Training takes place quickly with good results after the data are conditioned
Keywords :
acoustic signal processing; electric motors; feedforward neural nets; Galatea neural network simulator; acoustic performance; acoustic screening; aural cues; aural screening; screening process; small electric motors; sound pressure level; three-layer backpropagation neural network; Acoustic applications; Acoustic propagation; Acoustic testing; Assembly; Biological neural networks; Electric motors; Manufacturing automation; Manufacturing processes; Neural networks; Production;
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
DOI :
10.1109/IJCNN.1992.226943
