NDLI: Application of neural networks to acoustic screening of small electric motors

Content Provider	IEEE Xplore Digital Library
Author	Murphy, S.L. Sayegh, S.I.
Copyright Year	1992
Description	Author affiliation: Automation Engineering, Inc., Ft. Wayne, IN, USA (Murphy, S.L.)
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.
Starting Page	472
Ending Page	477
File Size	281939
Page Count	6
File Format	PDF
ISBN	0780305590
DOI	10.1109/IJCNN.1992.226943
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1992-06-07
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Acoustic applications Electric motors Manufacturing processes Biological neural networks Production Acoustic propagation Acoustic testing Manufacturing automation Assembly
Content Type	Text
Resource Type	Article

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Application of neural networks to acoustic screening of small electric motors