This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Choon Seong Leem Articles by Dreyfus, S. E. Search for Related Content Social Bookmarking                         What's this?

A Neural Network Approach to Feature Extraction in Sensor Signal Processing for Manufacturing Automation

Dept. of Industrial Engineering Rutgers University Piscataway, NJ 08855-0909

Stuart E. Dreyfus

Dept. of Industrial Engineering and Operations Research University of California at Berkeley Berkeley, CA 94720

Since off line hand-crafted conventional statistical feature selection/extraction methods are inefficient for signal-pattern classification of noisy sensor signals in manufacturing floor applications, we provide an automatic neural network approach to feature extraction, called Input Feature Scaling. Given only meaningful examples, the Input Feature Scaling algorithm enables a neural network, already trained by unsupervised competitive learning to cluster input patterns, to learn the relative importance of features for purposes of correct classification. The relative impor tance, expressed as weights, is adaptively learned in an additional supervised session. Experimental evaluation with both artificial data and real sensor data in tool-wear monitoring shows that our approach meets the requirements for "intelligent" sensor-signal processing in automated manufactur ing.

Journal of Intelligent Material Systems and Structures, Vol. 5, No. 2, 247-257 (1994)
DOI: 10.1177/1045389X9400500210


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?