Binary classification of items of interest in a repeatable process
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.
- Research Organization:
- National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- EE0002217
- Assignee:
- GM Global Technology Operations LLC (Detroit, MI)
- Patent Number(s):
- 8,757,469
- Application Number:
- 13/632,670
- OSTI ID:
- 1136396
- Country of Publication:
- United States
- Language:
- English
Method And System For Online Quality Monitoring And Control Of A Vibration Welding Process
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patent-application | May 2011 |
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