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

Volume

2

fYear

1992

fDate

7-11 Jun 1992

Firstpage

472

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1992. IJCNN., International Joint Conference on

Conference_Location

Baltimore, MD

Print_ISBN

0-7803-0559-0

Type

conf

DOI

10.1109/IJCNN.1992.226943

Filename

226943