Feature extraction networks for dull tool monitoring

Author

Owsley, Lane ; Atlas, Les ; Bernard, Gary

Author_Institution

Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA

Volume

5

fYear

1995

fDate

9-12 May 1995

Firstpage

3355

Abstract

Automatic feature extraction is a need in many current applications, including the monitoring of industrial tools. Currently available approaches suffer from a number of shortcomings. The Kohonen (1989) self-organizing neural network (SONN) has the potential to act as a feature extractor, but we find it benefits from several modifications. The purpose of these modifications is to cause feature variations to be aligned with the SONN indices so that the indices themselves can be used as measures of the features. The modified SONN is applied to the dull tool monitoring problem, and it is shown that the new algorithm extracts and characterizes useful features of the data

Keywords

computerised monitoring; feature extraction; machine tools; monitoring; self-organising feature maps; Kohonen self-organizing neural network; algorithm; automatic feature extraction; dull tool monitoring; feature extraction networks; industrial tools; Data mining; Feature extraction; Frequency; Humans; Interactive systems; Laboratories; Monitoring; Principal component analysis; Resonance; Spectrogram;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on

Conference_Location

Detroit, MI

ISSN

1520-6149

Print_ISBN

0-7803-2431-5

Type

conf

DOI

10.1109/ICASSP.1995.479704

Filename

479704