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Title: Binary classification of items of interest in a repeatable process

Abstract

A system includes host and learning machines in electrical communication with sensors positioned with respect to an item of interest, e.g., a weld, and memory. The host executes instructions from memory to predict a binary quality status of the item. The learning machine receives signals from the sensor(s), identifies candidate features, and extracts features from the candidates that are more predictive of the binary quality status relative to other candidate features. The learning machine maps the extracted features to a dimensional space that includes most of the items from a passing binary class and excludes all or most of the items from a failing binary class. The host also compares the received signals for a subsequent item of interest to the dimensional space to thereby predict, in real time, the binary quality status of the subsequent item of interest.

Inventors:
; ; ; ;
Publication Date:
Research Org.:
NETL (National Energy Technology Laboratory, Pittsburgh, PA, and Morgantown, WV (United States))
Sponsoring Org.:
USDOE
OSTI Identifier:
1136396
Patent Number(s):
8,757,469
Application Number:
13/632,670
Assignee:
GM Global Technology Operations LLC (Detroit, MI)
DOE Contract Number:  
EE0002217
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Abell, Jeffrey A., Spicer, John Patrick, Wincek, Michael Anthony, Wang, Hui, and Chakraborty, Debejyo. Binary classification of items of interest in a repeatable process. United States: N. p., 2014. Web.
Abell, Jeffrey A., Spicer, John Patrick, Wincek, Michael Anthony, Wang, Hui, & Chakraborty, Debejyo. Binary classification of items of interest in a repeatable process. United States.
Abell, Jeffrey A., Spicer, John Patrick, Wincek, Michael Anthony, Wang, Hui, and Chakraborty, Debejyo. Tue . "Binary classification of items of interest in a repeatable process". United States. https://www.osti.gov/servlets/purl/1136396.
@article{osti_1136396,
title = {Binary classification of items of interest in a repeatable process},
author = {Abell, Jeffrey A. and Spicer, John Patrick and Wincek, Michael Anthony and Wang, Hui and Chakraborty, Debejyo},
abstractNote = {A system includes host and learning machines in electrical communication with sensors positioned with respect to an item of interest, e.g., a weld, and memory. The host executes instructions from memory to predict a binary quality status of the item. The learning machine receives signals from the sensor(s), identifies candidate features, and extracts features from the candidates that are more predictive of the binary quality status relative to other candidate features. The learning machine maps the extracted features to a dimensional space that includes most of the items from a passing binary class and excludes all or most of the items from a failing binary class. The host also compares the received signals for a subsequent item of interest to the dimensional space to thereby predict, in real time, the binary quality status of the subsequent item of interest.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2014},
month = {6}
}

Patent:

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